206413943
Test Generation with Dynamic Probe Points in High Observability Testing Environment
High observability testing environment allows internal circuit nodes to be used as test points. However, such flexibility requires the development of new ATPG algorithm. Previous reported algorithm does not guarantee full fault-coverage and assumes all internal circuit nodes are test points. The new algorithm described in this paper will generate a full fault-coverage test set for a fanout free combinational circuit. The main characteristic of the algorithm is that it generates test vectors as well as probe points. As a result, the probe points are different for each test vector, and the number of probe points is the minimum for test set generated. Results obtained show that an average of 30% test vector reduction is achieved compared with the conventional test method which uses only output pins as test points.
206954852
A feedback control circuit design technique to suppress power noise in high speed output driver
In today's sub-micron CMOS integrated circuit technology, high speed output switching signals interacting with external inductance and capacitance produce noise which contaminates output signals and power buses. A Feedback Control Slew Rate Output Driver (FCSROD) which reduces the noise spike down to approximately 64% of a conventional output buffer without incurring the penalty of the propagation delay and even the rise/fall time is described. This effective power noise suppression is achieved by using distributed and weighted switching driver segments in conjunction with feedback control to control the output driver's slew rate. Dynamic short circuit current which is generated while both pFET and nFET are conducting is also minimized to reduce di/dt noise. FCSROD was compared with a conventional and the controlled slew rate output buffer, showing 64% noise reduction comparing to the conventional driver, and 22% improvement in both propagation delay and rise/fall time comparing with the controlled slew rate output driver.
894881
Performance improvement of TCP Vegas over heterogeneous networks
Current IP network has become the dominant paradigm for all networking environments. The significant cause of packet losses in such heterogenous networks is no longer limited to network congestion. Traditional TCP interprets every packet loss as caused by congestion which may be not the case in the current Internet. Misinterpretation of wireless random loss as an indication of network congestion results in TCP slowing down its sending rate unnecessarily. We propose a new variant of TCP Vegas named RedVegas. By using the innate nature of Vegas and congestion indications marked by routers, RedVegas may detect random packet losses precisely. Through the packet loss differentiation, RedVegas reacts appropriately to the losses, and therefore the throughput of connection over heterogeneous networks can be significantly improved.
30635651
A criterion for a multi-dimensional explosion due to a concentrated nonlinear source
Abstract This article studies a multi-dimensional parabolic first initial–boundary value problem with a concentrated nonlinear source. A criterion for its solution to blow up everywhere on the concentrated source in a finite time is given.
18544921
Active User-Based and Ontology-Based Web Log Data Preprocessing for Web Usage Mining
User identification and session identification are two major steps in preprocessing Web log data for Web usage mining. This paper introduces a fast active user-based user identification algorithm with time complexity O(n). The algorithm uses both an IP address and a finite users' inactive time to identify different users in the Web log. Web site ontology is useful for identifying Web site structure and break points for browsing behavior. For session identification, we present an ontology-based method that utilizes the Web site structure and functionalities to identify different sessions
7674871
Contextual vector quantization for speech recognition with discrete hidden Markov model
Abstract By using formulation of the finite mixture distribution identification, in this paper, several alternatives to the conventional LBG VQ method are investigated. A contextual VQ method based on the Markov Random Field (MRF) theory is proposed to model the speech feature vector space. Its superiority is confirmed by a series of comparative experiments in a speaker independent isolated word recognition task by using different VQ schemes as the front-end of DHMM. The motivation to use MRF to model the contextual dependence information in the underlying speech production process can be readily extended to acoustic modeling of the basic speech units in speech recognition.
18058814
Learning Rules from Very Large Databases Using Rough Multisets
This paper presents a mechanism called LERS-M for learning production rules from very large databases. It can be implemented using object-relational database systems, it can be used for distributed data mining, and it has a structure that matches well with parallel processing. LERS-M is based on rough multisets and it is formulated using relational operations with the objective to be tightly coupled with database systems. The underlying representation used by LERS-M is multiset decision tables, which are derived from information multisystems. In addition, it is shown that multiset decision tables provide a simple way to compute Dempster-Shafer’s basic probability assignment functions from. data sets.