We present novel concepts, technologies and potentials of optical data communication, especially for future computer architectures. The WDM (wavelength division multiplexing) and the optical wiring technologies are ex...
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This paper revisits a problem that was identified by Kramer and Magee: placing a system in a consistent state before and after runtime changes. We show that their notion of quiescence as a necessary and sufficient con...
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The advent of multicore processors has raised new demand for harnessing concurrency in the software mass market. We summarise our previous work on the data parallel, functional array processing language SaC. Its compi...
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ISBN:
(纸本)1595936904
The advent of multicore processors has raised new demand for harnessing concurrency in the software mass market. We summarise our previous work on the data parallel, functional array processing language SaC. Its compiler technology is geared towards highly runtime-efficient support for shared memory multiprocessors and, thus, is readily applicable to today's off-the-shelf multicore systems. Copyright 2007 ACM.
The use of induction motors is widespread in industry. Many researchers have studied the condition monitoring and detecting the faults of induction motors at an early stage. Early detection of motor faults results in ...
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The use of induction motors is widespread in industry. Many researchers have studied the condition monitoring and detecting the faults of induction motors at an early stage. Early detection of motor faults results in fast unscheduled maintenance. In this study, a new artificial immune based support vector machine algorithm is proposed for fault diagnosis of induction motors. Support vector machines (SVMs) have become one of the most popular classification methods in soft computing, recently. However, classification accuracy depends on kernel and penalty parameters. Artificial immune system has abilities of learning, memory and self adaptive control. The kernel and penalizes parameters of support vector machine are tuned using artificial immune system. The training data of support vector machine are extracted from three phase motor current. The new feature vector is constructed based on park's vector approach. The phase space of this feature vector is constructed using nonlinear time series analysis. Broken rotor bar and stator short circuit faults are classified in combined phase space using support vector machines. The experimental data are taken from a three phase induction motor. One, two and three broken rotor bar faults and 10% short circuit of stator faults are detected successfully.
An important aspect of system support for mobile computing involves alleviating the issues related to programming the underlying distributed system. Our approach to dealing with these issues is by means of programming...
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There are many benefits to participating in multi-campus collaborations among project-based design teams. First, students gain experience in working in a distributed design environment, which is becoming more commonpl...
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This paper revisits a problem that was identified by Kramer and Magee: placing a system in a consistent state before and after runtime changes (1990). We show that their notion of quiescence as a necessary and suffici...
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This paper revisits a problem that was identified by Kramer and Magee: placing a system in a consistent state before and after runtime changes (1990). We show that their notion of quiescence as a necessary and sufficient condition for safe runtime changes is too strict and violates the black-box design principle. We introduce a weaker condition, tranquility; easier to obtain, less disruptive for the system and still sufficient to ensure application consistency. We also present an implementation of this concept in a component middleware platform
Cyber theft is a serious threat to Internet security. It is one of the major security concerns by both network service providers and Internet users. Though sensitive information can be encrypted when stored in non-vol...
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Cyber theft is a serious threat to Internet security. It is one of the major security concerns by both network service providers and Internet users. Though sensitive information can be encrypted when stored in non-volatile memory such as hard disks, for many e-commerce and network applications, sensitive information is often stored as plaintext in main memory. Documented and reported exploits facilitate an adversary stealing sensitive information from an application's memory. These exploits include illegitimate memory scan, information theft oriented buffer overflow, invalid pointer manipulation, integer overflow, password stealing Trojans and so forth. Today's computing system and its hardware cannot address these exploits effectively in a coherent way. This paper presents a unified and lightweight solution, called InfoShield that can strengthen application protection against theft of sensitive information such as passwords, encryption keys, and other private data with a minimal performance impact. Unlike prior whole memory encryption and information flow based efforts, InfoShield protects the usage of information. InfoShield ensures that sensitive data are used only as defined by application semantics, preventing misuse of information. Comparing with prior art, InfoShield handles a broader range of information theft scenarios in a unified framework with less overhead. Evaluation using popular network client-server applications shows that InfoShield is sound for practical use and incurs little performance loss because InfoShield only protects absolute, critical sensitive information. Based on the profiling results, only 0.3% of memory accesses and 0.2% of executed codes are affected by InfoShield.
In robust regression we often have to decide how many are the unusualobservations, which should be removed from the sample in order to obtain better fitting for the restof the observations. Generally, we use the basic...
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In robust regression we often have to decide how many are the unusualobservations, which should be removed from the sample in order to obtain better fitting for the restof the observations. Generally, we use the basic principle of LTS, which is to fit the majority ofthe data, identifying as outliers those points that cause the biggest damage to the robust ***, in the LTS regression method the choice of default values for high break down-point affectsseriously the efficiency of the estimator. In the proposed approach we introduce penalty cost fordiscarding an outlier, consequently, the best fit for the majority of the data is obtained bydiscarding only catastrophic observations. This penalty cost is based on robust design weights andhigh break down-point residual scale taken from the LTS estimator. The robust estimation is obtainedby solving a convex quadratic mixed integer programming problem, where in the objective functionthe sum of the squared residuals and penalties for discarding observations is minimized. Theproposed mathematical programming formula is suitable for small-sample data. Moreover, we conduct asimulation study to compare other robust estimators with our approach in terms of their efficiencyand robustness.
A new field in distributed computing, called Ambient Intelligence, has emerged as a consequence of the increasing availability of wireless devices and the mobile networks they induce. Developing software for such mobi...
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