Ensuring a high manufacturing test quality of an integrated electronic circuit mandates the application of a large volume
test set. Even if the test data can be fit into the memory of an external tester, the consequent increase in test application
time reflects into elevated production costs. Test data compression solutions have been proposed to address the test time
and data volume problem by storing and delivering the test data in a compressed format, and subsequently by expanding the
data on-chip. In this paper, we propose a scan cell positioning methodology that accompanies a compression technique in order
to boost the compression ratio, and squash the test data even further. While we present the application of the proposed approach
in conjunction with the fan-out based decompression architecture, this approach can be extended for application along with
other compression solutions as well. The experimental results also confirm the compression enhancement of the proposed methodology. 相似文献
Due to the increasing availability and sophistication of data recording techniques, multiple information sources and distributed
computing are becoming the important trends of modern information systems. Many applications such as security informatics
and social computing require a ubiquitous data analysis platform so that decisions can be made rapidly under distributed and
dynamic system environments. Although data mining has now been popularly used to achieve such goals, building a data mining
system is, however, a nontrivial task, which may require a complete understanding on numerous data mining techniques as well
as solid programming skills. Employing agent techniques for data analysis thus becomes increasingly important, especially
for users not familiar with engineering and computational sciences, to implement an effective ubiquitous mining platform.
Such data mining agents should, in practice, be intelligent, complete, and compact. In this paper, we present an interactive
data mining agent — OIDM (online interactive data mining), which provides three categories (classification, association analysis,
and clustering) of data mining tools, and interacts with the user to facilitate the mining process. The interactive mining
is accomplished through interviewing the user about the data mining task to gain efficient and intelligent data mining control.
OIDM can help users find appropriate mining algorithms, refine and compare the mining process, and finally achieve the best
mining results. Such interactive data mining agent techniques provide alternative solutions to rapidly deploy data mining
techniques to broader areas of data intelligence and knowledge informatics. 相似文献
Logic programming under the stable model semantics is proposed as a non-monotonic language for knowledge representation and
reasoning in artificial intelligence. In this paper, we explore and extend the notion of compatibility and the Λ operator, which were first proposed by Zhang to characterize default theories. First, we present a new characterization
of stable models of a logic program and show that an extended notion of compatibility can characterize stable submodels. We further propose the notion of weak auto-compatibility which characterizes the Normal Forward Chaining Construction proposed by Marek, Nerode and Remmel. Previously, this construction was only known to construct the stable models of FC-normal
logic programs, which turn out to be a proper subclass of weakly auto-compatible logic programs. We investigate the properties
and complexity issues for weakly auto-compatible logic programs and compare them with some subclasses of logic programs. 相似文献
Peer-to-Peer Desktop Grid (P2PDG) has emerged as a pervasive cyber-infrastructure tackling many large-scale applications with
high impacts. As a burgeoning research area, P2PDG can support numerous applications, including scientific computing, file
sharing, web services, and virtual organization for collaborative activities and projects. To handle trustworthiness issues
of these services, trust and reputation schemes are proposed to establish trust among peers in P2PDG. In this paper, we propose
a robust group trust management system, called H-Trust, inspired by the H-index aggregation technique. Leveraging the robustness
of the H-index algorithm under incomplete and uncertain circumstances, H-Trust offers a robust personalized reputation evaluation
mechanism for both individual and group trusts with minimal communication and computation overheads. We present the H-Trust
scheme in five phases, including trust recording, local trust evaluation, trust query phase, spatial-temporal update phase,
and group reputation evaluation phases. The rationale for its design, the analysis of the algorithm are further investigated.
To validate the performance of H-Trust scheme, we designed the H-Trust simulator HTrust-Sim to conduct multi-agent-based simulations.
Simulation results demonstrate that H-Trust is robust and can identify and isolate malicious peers in large scale systems
even when a large portion of peers are malicious. 相似文献