38698913
A unified fault model and test generation procedure for interconnect opens and bridges
A unified gate-level fault model for interconnect opens and bridges is proposed. Defects are modeled as constrained multiple line stuck-at faults. A novel feature of the proposed fault model is its flexibility to accommodate increasing levels of accuracy. Additionally the model does not require accurate device level circuit models to achieve desired accuracy. Efficient methods for fault simulation and test generation are discussed and experimental results on benchmark circuits and industrial designs are presented. The experimental results presented show that the tests generated using simpler versions of the proposed fault model achieve higher defect coverage than the tests using two currently popular methods to derive high defect coverage tests.
21571419
N-distinguishing Tests for Enhanced Defect Diagnosis
Diagnostic ATPG has traditionally been used to generate test patterns that distinguish pairs of modeled faults. In this work, we investigate the use of n-distinguishing test sets, which distinguish pairs of single stuck-at faults n times, to enhance the probability of distinguishing unmodeled defects. The basis for the use of n-distinguishing test sets to enhance defect diagnosis is similar to that for using n-detection test sets to improve the detection of unmodeled defects. We use a heuristic to target a subset of fault pairs for n-distinguishing in order to improve the efficacy of the patterns generated for aiding diagnosis. Experimental results on the larger ISCAS benchmark circuits are presented to demonstrate the improvements in defect diagnostic resolution due to the use of n-distinguishing test sets. We use randomly selected resistive bridges to represent unmodeled defects. The experimental results also show that the coverage of unmodeled defects by n-distinguishing test sets is similar to that by n-detection test sets even though the number of n-distinguishing tests is typically smaller. This suggests the possibility of using n-distinguishing test sets in place of n-detection test sets in manufacturing test.
11514442
A test pattern ordering algorithm for diagnosis with truncated fail data
In this paper, we propose a test pattern ordering algorithm for fault diagnosis. Test pattern ordering is effective in situations where the fail log is truncated and contains a limited number of fail data. In such cases, higher diagnostic resolution can be achieved with the test set appropriately ordered. Test pattern ordering is independent of the diagnosis algorithm used. The higher resolution achieved by test pattern ordering is obtained at no additional cost once the test patterns have been appropriately ordered. Experimental results on two industrial designs are presented to demonstrate the effectiveness of the proposed method
6824407
Defect aware test patterns
A method to generate test patterns referred to as defect aware test patterns is proposed. Defect aware test patterns increase the ability to detect unmodeled defects. The proposed method can be used with any test generation procedure to improve the effectiveness of the tests in detecting unmodeled defects. Experimental results on several industrial designs show the effectiveness of defect aware tests. We also propose a measure to estimate the effectiveness of given test sets in detecting unmodeled defects.
22493357
Procedures for identifying untestable and redundant transition faults in synchronous sequential circuits
Due to their simplicity transition faults are often used as targets for test generation to detect delay defects. However, one concern documented in the literature is that of overtesting. One of the reasons for overtesting is that DFT approaches, such as scan, change sequentially untestable faults into testable faults. One approach to reducing overtesting is to identify sequentially untestable and redundant faults and not target them during test generation for the circuit with scan. Another application of identifying untestable transition faults is its use in logic optimization. We investigate efficient procedures to identify untestable and redundant transition faults in nonscan synchronous sequential circuits. Experimental results for ISCAS-89 benchmark circuits are presented.
14482306
New procedures to identify redundant stuck-at faults and removal of redundant logic
We present new procedures for identifying redundant stuck-at faults including multiple line stuck-at faults on the branches of fan-out stems. The methods proposed include new procedures to identify stuck-at faults that are simultaneously redundant thus allowing simultaneous removal of logic associated with several redundant faults. Experimental results on benchmark as well as industrial circuits are also presented to demonstrate the effectiveness of the proposed methods.
37994970
Image coding quality assessment using fuzzy integrals with a three-component image model
Based on importance measures and fuzzy integrals, a new assessment method for image coding quality is presented in this paper. The proposed assessment is based on two subevaluations. In the first subevaluation, errors on edges, textures, and flat regions are computed individually. The errors are then assessed using an assessment function. A global evaluation with Sugeno fuzzy integral is then obtained based on the importance measure of edge, texture, and flat region. In the second subevaluation, an importance measure is first established depending on the types of regions where errors occur, a subtle evaluation is then obtained using Sugeno fuzzy integral on all pixels of the image. A final evaluation is obtained based on the two subevaluations. Experimental results show that this new image quality assessment closely approximates human subjective tests such as mean opinion score with a high correlation coefficient of 0.963, which is a significant improvement over peak signal-to-noise ratio, picture quality scale, and weighted mean square error, three other image coding quality assessment methods, which have the correlation coefficients of 0.821, 0.875, and 0.716, respectively.
44399237
Mining Infrequently-Accessed File Correlations in Distributed File System
File correlation mining, as a technique to enhance file system performance, can usually be exploited for many purposes such as to improve the effectiveness of cache, to optimize file layout, as well as to enable disk file prefetching. While most research works on file correlations focus on traditional stand-alone file systems, this paper investigates the problem of mining file correlations in a distributed environment. We present a parallel data mining algorithm called PFC-Miner (Parallel File Correlation Miner), which is based on Locality Sensitive Hashing. PFC-Miner can efficiently discover correlations between infrequently-accessed files which are more valuable for web applications. Experimental results show that PFC-Miner can efficiently discover file correlations in distributed file systems without compromising the accuracy, and that the proposed approach has good scalability.
13987553
Assessment of Color Video Quality with Singular Value Decomposition of Complex Matrix
Most proposed video quality assessment approaches in the literature are only applied to the luminance layer of frame. Others are applied to luminance and chrominance layers respectively and then pooling by means of grayscale image quality metrics. In this paper, we proposed a full-reference objective video quality assessment called CMSVD,which treats luminance and chrominance parts as a whole and considers multiple factors of the human visual system(HVS)and video features. The algorithm is tested on the video quality experts group (VQEG) phase I FR-TV test data set.Experiments show that it has good correlation with perceived video quality.
2501005
Coordinating Multiple Agents via Reinforcement Learning
In this paper, we attempt to use reinforcement learning techniques to solve agent coordination problems in task-oriented environments. The Fuzzy Subjective Task Structure model (FSTS) is presented to model the general agent coordination. We show that an agent coordination problem modeled in FSTS is a Decision-Theoretic Planning (DTP) problem, to which reinforcement learning can be applied. Two learning algorithms, ‘‘coarse-grained’’ and ‘‘fine-grained’’, are proposed to address agents coordination behavior at two different levels. The ‘‘coarse-grained’’ algorithm operates at one level and tackle hard system constraints, and the ‘‘fine-grained’’ at another level and for soft constraints. We argue that it is important to explicitly model and explore coordination-specific (particularly system constraints) information, which underpins the two algorithms and attributes to the effectiveness of the algorithms. The algorithms are formally proved to converge and experimentally shown to be effective.
39222179
A New ART Neural Networks for Remote Sensing Image Classification
A new ART2A-C algorithm based on fuzzy operators to cluster the remote sensing images and aerials is proposed in this paper. By combining two ART ANNs with higher performance, the traditional ART2A-C is developed with the fuzzy operators introduced in matching rule. Then the proposed method is applied to the classification and the new network is implemented as well as two other existed ARTs respectively. Experimental results show that the new method outperforms the traditional ones.