Static program analysis techniques cannot know certain values, such as the value of user input or network state, at analysis time. While such unknown values need to be treated as nondeterministic choices made by the p...
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Static program analysis techniques cannot know certain values, such as the value of user input or network state, at analysis time. While such unknown values need to be treated as nondeterministic choices made by the program's execution environment, it is still possible to glean very useful information about how such statically unknown values may or must influence computation. We give a method for integrating such nondeterministic choices with an expressive static analysis. Interestingly, we cannot solve the resulting recursive constraints directly, but we give an exact method for answering all may and must queries. We show experimentally that the resulting solved forms are concise in practice, enabling us to apply the technique to very large programs, including an entire operating system.
This paper highlights key advances in the presilicon verification effort of the IBM zEnterprise (R) 196 (z196) microprocessor and storage hierarchy. It focuses on the unique set of verification challenges as well as t...
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This paper highlights key advances in the presilicon verification effort of the IBM zEnterprise (R) 196 (z196) microprocessor and storage hierarchy. It focuses on the unique set of verification challenges as well as the process innovations that address them. At the time of product launch, the z196 system represented the industry's fastest and most scalable enterprise system, with up to 80 customer-configurable out-of-order core processors operating at 5.2 GHz. In addition to offering industry-leading performance, the z196 system builds upon its leadership in reliability by introducing a new redundant array of independent memory (RAIM) technology into its memory subsystem. The new product features in this system drove innovations in all aspects of processor functional verification, including stimulus generation, functional checking, debugging, and coverage. A new hybrid RAIM verification methodology, which includes both formal and random methods, is described. Many process and methodology improvements were made to improve developmental collaboration across a global team. These enhancements include a simulation development environment that uses common shared components across functional partitions, as well as a shared cache loader that was used across multiple environments. We also present a self-configuring test-case generation process that focused on the coverage of functional stimulus.
debugging HIGH-END EMBEDDED APPLICATIONS IS NO LONGER STATIC. SEMICONDUCTOR VENDORS, TOOL DEVELOPERS, AND INSTRUMENT MAKERS HAVE COMBINED TO SUPPORT THE NEXUS STANDARD, WHICH RELIES ON ON-CHIP HARDWARE FEATURES AND DE...
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debugging HIGH-END EMBEDDED APPLICATIONS IS NO LONGER STATIC. SEMICONDUCTOR VENDORS, TOOL DEVELOPERS, AND INSTRUMENT MAKERS HAVE COMBINED TO SUPPORT THE NEXUS STANDARD, WHICH RELIES ON ON-CHIP HARDWARE FEATURES AND DEFINES A RIGOROUS TOOL METHODOLOGY TO ASSIST IN REAL-TIME debugging OF EMBEDDED APPLICATIONS.
Objective: The author presents and discusses discoveries and developments contributing to enhanced electronics maintenance performance. Background: This body of research is viewed from the vantage point of Nick Bond&#...
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Objective: The author presents and discusses discoveries and developments contributing to enhanced electronics maintenance performance. Background: This body of research is viewed from the vantage point of Nick Bond's 1970 Ely Award-winning article in Human Factors, "Some Persistent Myths About Military Electronics Maintenance." Method: Bond identified a set of myths and summarized research that not only produced information and techniques leading to demonstrably improved maintenance performance but also exploded many unfounded beliefs that were commonly held before the research had been conducted and the findings disseminated. Results: The period from 1964 through 1986, as reflected by publications in the journal, was a productive period of research that led to greater understanding of human factors in electronics maintenance and to numerous advances that contributed, ultimately, to more effective maintenance performance. Conclusion: Technological advances, combined with what we learned about maintenance performance, have substantially reduced the maintenance burden and enhanced the maintenance of electronic systems. Application: Some of the principal lessons learned from this research on electronics maintenance apply to understanding the effects of equipment complexity, providing an optimal role for automation, designing more appropriate on-the-job training, and enhancing troubleshooting skills.
An introduction is presented in which the editor discusses various reports within the issue on topics including managed semantic interoperability, semantic priority scheduling for multidomain active measurements, and ...
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An introduction is presented in which the editor discusses various reports within the issue on topics including managed semantic interoperability, semantic priority scheduling for multidomain active measurements, and fault handling.
Focuses on an approach to visual debugging that uses the innate visual pattern recognition skills as a part of the debugging process. Components of the debugging model; Attributes of the components of the system; Appl...
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Focuses on an approach to visual debugging that uses the innate visual pattern recognition skills as a part of the debugging process. Components of the debugging model; Attributes of the components of the system; Applications of the visual debugging approach; Hinderance in the debugging process by the scientific visualization techniques.
The shrew DDoS attacks are stealth low-rate TCP-targeted DDoS attacks, which conceal their malicious activities into normal traffic. Although the good pretense eludes them from being detected in time domain, the exist...
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The shrew DDoS attacks are stealth low-rate TCP-targeted DDoS attacks, which conceal their malicious activities into normal traffic. Although the good pretense eludes them from being detected in time domain, the existent energy exposes them in frequency domain. Online Power Spectral Density (PSD) analysis necessitates real-time PSD data conversion is a must. In this paper, an optimized FPGA based real-time PSD converter is proposed, which is based on our innovative component-reusable Auto-Correlation (AC) algorithm and the adapted 2N-point real-valued Discrete Fourier Transform (DFT) algorithm. Further optimization is achieved through the exploration of algorithm characters and hardware parallelism for this case. The evaluation results from both simulation and synthesis are provided. The conversion of a 512-point data sequence can be finished in the time interval of sampling one data point;and the overall design could be easily fitted in a Xilinx Virtex2 Pro FGPA. (C) 2012 Elsevier Ltd. All rights reserved.
A distributed leader follower formation tracking controller is presented in this paper. The dynamics of each agent are modeled by Euler Lagrange equations, and all agents are guaranteed to track a desired timevarying ...
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A distributed leader follower formation tracking controller is presented in this paper. The dynamics of each agent are modeled by Euler Lagrange equations, and all agents are guaranteed to track a desired timevarying trajectory in the workspace. The system uncertainties and external disturbances, which are equivalently described by a bounded additive noise, are considered in the controller design, and the proposed controller is robust to noise. Fault diagnosis of the nonlinear multi-agent system is also discussed with the help of differential geometry tools and an active fault recovery strategy is incorporated into the proposed control scheme. The effectiveness of the proposed controller is verified through simulations. (C) 2014 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
In traditional principle component analysis (PCA), because of the neglect of the dimensions influence between different variables in the system, the selected principal components (PCs) often fail to be representative....
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In traditional principle component analysis (PCA), because of the neglect of the dimensions influence between different variables in the system, the selected principal components (PCs) often fail to be representative. While the relative transformation PCA is able to solve the above problem, it is not easy to calculate the weight for each characteristic variable. In order to solve it, this paper proposes a kind of fault diagnosis method based on information entropy and Relative Principle Component Analysis. Firstly, the algorithm calculates the information entropy for each characteristic variable in the original dataset based on the information gain algorithm. Secondly, it standardizes every variable's dimension in the dataset. And, then, according to the information entropy, it allocates the weight for each standardized characteristic variable. Finally, it utilizes the relative-principal-components model established for fault diagnosis. Furthermore, the simulation experiments based on Tennessee Eastman process and Wine datasets demonstrate the feasibility and effectiveness of the new method.
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