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.
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.
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.
40279092
An Audio Feature Extraction Method Taking Class Information into Account
An inverter ballast for fluorescent lamps utilizing a resonant circuit coupled to the lamp and operated from a source of DC power through transistors. The transistors are controlled by a sensing means in the resonant loop which insures that the transistor switch at zero current. Furthermore, the coupling means for the load controls a source of heater voltage to the anodes of the fluorescent light which operate at very low current, an antisaturation circuit made up of separate windings are on the current transformer and connected to the switching transistor bases and through diodes to the power supply to prevent "shoot through" current in the transistors. An autotransformer is used as a high leakage reactance transformer; and a circuit is provided to prevent overload of the resonant circuit in case of lamp failure. Shoot through currents are prevented, and overload of the resonant circuit is prevented, and an autotransformer coupling of the resonant circuit to the load is used.
2098837
Semi-Supervised Regression for Evaluating Convenience Store Location
Location plays a very important role in the retail business due to its huge and long-term investment. In this paper, we propose a novel semisupervised regression model for evaluating convenience store location based on spatial data analysis. First, the input features for each convenience store can be extracted by analyzing the elements around it based on a geographic information system, and the turnover is used to evaluate its performance. Second, considering the practical application scenario, a manifold regularization model with one semi-supervised performance information constraint is provided. The promising experimental results in the real-world dataset demonstrate the effectiveness of the proposed approach in performance prediction of certain candidate locations for new convenience store opening.
13259771
Deploying HEP Applications on Multiple Grid Infrastructures
High energy physics (HEP) experiments are facing unprecedented challenges to deal with huge volumes of data. Grid is the excellent technology for it. However, Incompatibility of a variety of grid middewares is an obstacle to exploit the resources on multiple grid infrastructures. This paper firstly introduces HEP experiments and HEP grid, and then reviews the solutions for grid interoperability and interoperations. Because current HEP applications are tightly with gLite middleware, this paper highlights a gateway approach to interoperate with gLite and described it in detail. Direct access to gLite storage system is very important, and a dedicated tool for HEP data sharing is then proposed and implemented. Finally, the implementation of the interoperability of GOS and gLite is introduced.
18102767
Resilient Rights Protection for Product Data
With the development of advance manufacture applications, enterprise must interact with their suppliers and customers to share more product data information. Consequently, copyright and integrity protection over product data is becoming an urgent requirement. Product data information consists of two parts: product structure and part content, besides protecting the three-dimensional (3D) model of part by watermarking, we also need to protect product structure information, which describes the relations among product parts and can be represented as a tree. In this paper, based on the 3D polygonal meshes watermark technology, the structure/semi-structure data watermark technologies and the idea of Fractal, we propose a novel watermark scheme for tree-structure product data with the value lying both in product structure and in part node content, which gives a more comprehensive and resilient right protection for both part node content and product structure. Moreover it is particularly effective when resisting invertibility attacks
17685609
Efficient multi-label classification with hypergraph regularization
Many computer vision applications, such as image classification and video indexing, are usually multi-label classification problems in which an instance can be assigned to more than one category. In this paper, we present a novel multi-label classification approach with hypergraph regularization that addresses the correlations among different categories. First, a hypergraph is constructed to capture the correlations among different categories, in which each vertex represents one training instance and each hyperedge for one category contains all the instances belonging to the same category. Then, an improved SVM like learning system incorporating the hypergraph regularization, called Rank-HLapSVM, is proposed to handle the multi-label classification problems. We find that the corresponding optimization problem can be efficiently solved by the dual coordinate descent method. Many promising experimental results on the real datasets including ImageCLEF and MediaMill demonstrate the effectiveness and efficiency of the proposed algorithm.