Output hazard-free transition tests for silicon calibrated scan based delay testing
Architectural restrictions of scan greatly limit the effectiveness of traditional scan based delay tests. It has been recently shown that additional testing for delays on short paths using fast clocks can significantly lower DPM. However, accurately obtaining the needed timing information for such tests from simulation is extremely difficult. The simulations must not only accurately account for the effects of process parameter variations, but also power supply noise and crosstalk from the excessive switching activity of scan tests. We suggest that learning signal timing information on silicon to "calibrate" such tests can be much more accurate and cost effective. However, such an approach requires that the outputs of the applied tests be hazard free to avoid learning incorrect timing due to a glitch at the output. Simulation results presented here indicate that such output hazard free test can be obtained with an average coverage only about 10% below the transition delay fault coverage for both launch-on-shift (LOS) and launch-on-capture (LOG) modes

title: Delay Test Scan Flip-Flop: DFT for High Coverage Delay Testing
abstract: Scan based delay testing is currently mostly implemented using launch-on-capture (LOC) delay tests. Launch-on-shift (LOS) tests are generally more effective, achieving higher fault coverage with significantly fewer test vectors, but require a fast scan enable, which is not supported by most designs. The paper presents low cost solution for implementing LOS tests by adding a small amount of logic (six transistors) in each flip-flop to align the slow scan enable signal to the clock edge. The new design can support full LOS and LOC testing, achieving an average TDF coverage of 92.67% in this combined mode for the ISCAS89 benchmarks. Adding a second slow global scan enable signal also allows mixed LOC/LOS tests, which can further increase coverage up to 94.86% on average for ISCAS89 benchmarks

title: Delay Defect Characterization Using Low Voltage Test
abstract: For nanometer designs, many subtle defects lead to excessive delays in signal paths that cause reliability concerns. Traditional test-based diagnosis methods can only identify the failing nodes without the capability to tell the defect nature behind the observed delay faults. This differentiation is important for gathering accurate defect statistics for process improvement during yield ramp-up. In this paper we presented an effective delay defect analysis methodology that can quickly categorize the delay defects into either transistor related defects or resistive interconnect defects. The new delay defect/failure characterization method is based on low voltage test and delay defect detection in slack interval (DDSI) method. Experimental results were presented to validate the effectiveness of the new method. Practical considerations were also addressed for adoption of the methodology.

title: Low voltage test in place of fast clock in DDSI delay test
abstract: By testing the CUT at lower supply voltages, the CUT slows down and thus slow, low-cost testers can be used to perform DDSI (defect detection within slack intervals) tests. Apart from this, because the delay fault size is known in a DDSI test, this information can be further used to diagnose the causing mechanism behind the delay faults. Experimental results are presented to investigate the potential of the method.

title: Hidden Markov Model Based Events Detection in Soccer Video
abstract: This paper presents an algorithm developed based on hidden Markov model for cues fusion and event inference in soccer video. Four events, shoot, foul, offside and normal playing, are defined to be detected. The states of the events are employed to model the observations of the five cues, which are extracted from the shot sequences directly. The experimental results show the algorithm is effective and robust in inferring events from roughly extracted cues.

title: Learning object intrinsic structure for robust visual tracking
abstract: In this paper, a novel method to learn the intrinsic object structure for robust visual tracking is proposed. The basic assumption is that the parameterized object state lies on a low dimensional manifold and can be learned from training data. Based on this assumption, firstly we derived the dimensionality reduction and density estimation algorithm for unsupervised learning of object intrinsic representation, the obtained non-rigid part of object state reduces even to 2 dimensions. Secondly the dynamical model is derived and trained based on this intrinsic representation. Thirdly the learned intrinsic object structure is integrated into a particle-filter style tracker. We will show that this intrinsic object representation has some interesting properties and based on which the newly derived dynamical model makes particle-filter style tracker more robust and reliable. Experiments show that the learned tracker performs much better than existing trackers on the tracking of complex non-rigid motions such as fish twisting with self-occlusion and large inter-frame lip motion. The proposed method also has the potential to solve other type of tracking problems.

title: Minimal Mean-Curvature-Variation Surfaces and Their Applications in Surface Modeling
abstract: Physical based and geometric based variational techniques for surface construction have been shown to be advanced methods for designing high quality surfaces in the fields of CAD and CAGD. In this paper, we derive a Euler-Lagrange equation from a geometric invariant curvature integral functional–the integral about the mean curvature gradient. Using this Euler-Lagrange equation, we construct a sixth-order geometric flow (named as minimal mean-curvature-variation flow), which is solved numerically by a divided-difference-like method. We apply our equation to solving several surface modeling problems, including surface blending, N-sided hole filling and point interpolating. The illustrative examples provided show that this sixth-order flow yields high quality surfaces.

title: Acoustic scattering on arbitrary manifold surfaces
abstract: We propose the use of surface subdivision as adaptive and higher-order boundary elements for solving a Helmholtz partial differential equation to calculate accurate acoustic scattering on arbitrary manifolds. Such acoustic transfer functions prove useful for designing and tuning hearing aid devices for hearing impaired individuals. The number of unknowns of the discretized linear system is the same as that in a linear element approach. Our results show that the accuracy of the subdivision approach is much better than that of the linear element approach.

title: 3D model based expression tracking in intrinsic expression space
abstract: A method of learning the intrinsic facial expression space for expression tracking is proposed. First, a partial 3D face model is constructed from a trinocular image and the expression space is parameterized using MPEG4 FAP. Then an algorithm of learning the intrinsic expression space from the parameterized FAP space is derived. The resulted intrinsic expression space reduces even to 5 dimensions. We will show that the obtained expression space is superior to the space obtained by PCA. Then the dynamical model is derived and trained on this intrinsic expression space. Finally, the learned tracker is developed in a particle-filter-style tracking framework. Experiments on both synthetic and real videos show that the learned tracker performs stably over a long sequence and the results are encouraging.