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.
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.
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
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.
17959493
A Fast Document Classification Algorithm Based on Improved KNN
A novel KNN classification algorithm combining model and evidence theory is proposed in this paper. The new method not only overcomes the main shortage of lazy learning in traditional KNN, but also takes the distances between samples to be recognized and samples in k-neighbors into account. At the same time the method resolves the unrecognizable cases of unknown samples. Applying the classification algorithm into the document recognition, experimental results show its satisfied recognition rate and fast categorization speed
11779893
Music retrieval based on a multi-samples selection strategy for support vector machine active learning
In active learning based music retrieval systems, providing multiple samples to the user for feedback is very necessary. In this paper, we present a new multi-samples selection strategy designed for support vector machine active learning. Aiming to reduce the redundancy between the selected samples, the strategy enforces the selected samples to be diverse by explicitly maximizing the distance between each other in the feature space. Experimental results on a music genre database demonstrated the effectiveness of the proposed strategy in selecting relevant multiple samples for human feedback on them.
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.
110367358
Unified synthesis techniques for high performance fpga designs
As the device geometry keeps shrinking in today's deep sub-micron technologies, interconnect delay dominates the critical path. In the current FPGA design flow, there are two major drawbacks: the separation of technology-independent optimization and technology mapping in the traditional logic synthesis flow, and the separation of logic synthesis and physical implementation in the traditional implementation flow. Such drawbacks result in sub-optimal performance and a long timing closure cycle. In this thesis we propose the utilization of unified synthesis techniques in the context of FPGAs to address these issues and improve the overall design performance. In Chapter 3 we bridge the gap between technology-independent optimization and technology mapping by combining logic decomposition and mapping. In Chapter 4, we introduce a novel algorithm named SCPlace [17] to perform simultaneous clustering during placement. In Chapter 5 we introduce an algorithm named SPD to perform logic duplication during placement. In Chapter 6 we build the first complete academic CAD flow, which can be easily plugged in to a state-of-the-art commercial tool, Altera's Quartus 4.0 software. Our unified synthesis and placement tool, SPCD, combines both the clustering and duplication techniques. Our experimental results show up to over 30% improvement in performance compared with the current state-of-the-art academic flow.
537363
Change propagation algorithm in a unified feature modeling scheme
Product lifecycle stages are inter-related and mutually constraining. Due to the sequential nature of the product development processes, some constraints or conflicts may emerge in a later stage and require modifications to the decisions made in earlier stages. The iterations between stages are hence unavoidable and must be managed carefully to maintain the consistency, integrity and validity of product information models. Due to the inter- or intra-stage relations, a chain of changes is very likely to occur as the consequence of an initial change. Modeling and maintaining these relations are important in collaborative engineering to evolve the state of the whole product model in a consistent manner. This paper introduces a new method of modeling associative engineering relations in a unified feature modeling scheme and elaborates a change propagation algorithm for the information consistency control among multiple applications of product lifecycle stages. The algorithm is established on a JTMS-based dependency network. Two case studies are used to illustrate the proposed dependency network and change propagation algorithm.