Generating discriminating cartoon faces using interacting snakes

As a computational bridge between the high-level a priori knowledge of object shape and the low-level image data, active contours (or snakes) are useful models for the extraction of deformable objects. We propose an approach for manipulating multiple snakes iteratively, called interacting snakes, that minimizes the attraction energy functionals on both contours and enclosed regions of individual snakes and the repulsion energy functionals among multiple snakes that interact with each other. We implement the interacting snakes through explicit curve (parametric active contours) representation in the domain of face recognition. We represent human faces semantically via facial components such as eyes, mouth, face outline, and the hair outline. Each facial component is encoded by a closed (or open) snake that is drawn from a 3D generic face model. A collection of semantic facial components form a hypergraph, called semantic face graph, which employs interacting snakes to align the general facial topology onto the sensed face images. Experimental results show that a successful interaction among multiple snakes associated with facial components makes the semantic face graph a useful model for face representation, including cartoon faces and caricatures, and recognition.     

MODEL‐REFERENCE OUTPUT‐FEEDBACK SLIDING MODE CONTROLLER FOR A CLASS OF MULTIVARIABLE NONLINEAR SYSTEMS

This paper proposes an output‐feedback sliding mode control design for a class of uncertain multivariable plants with nonlinear disturbances. The approach used here is based on the control parameterization employed in model‐reference adaptive control. The disturbances are allowed to be unmatched and to depend not only on the plant output but also on its unmeas‐urable state. A less restrictive condition on the uncertainty of the high frequency gain matrix is also obtained.     

A User-Aware Prefetching Mechanism for Video Streaming

The randomly and unpredictable user behaviors during a multimedia presentation may cause the long retrieval latency in the client–server connection. To accommodate the above problem, we propose a prefetching scheme that using the association rules from the data mining technique. The data mining technique can provide some priority information such as the support, confidence, and association rules which can be utilized for prefetching continuous media. Thus, using the data mining technique, the proposed prefetching policy can predict user behaviors and evaluate segments that may be accessed in near future. The proposed prefetching scheme was implemented and tested on synthetic data to estimate its effectiveness. Performance experiments show that the proposed prefetching scheme is effective in improving the latency reduction, even for small cache sizes.     

Miniature erbium:ytterbium fiber Fabry-Perot multiwavelength lasers

We demonstrate stable simultaneous lasing of up to 29 wavelengths in miniature 1- and 2-mm-long Er³⁺:Yb³⁺ fiber Fabry-Perot lasers. The wavelengths are separated by 0.8 (100 GHz) and 0.4 nm (50 GHz), respectively, corresponding to the free spectral range of the laser cavity. The number of lasing wavelengths and the power stability of the individual modes are greatly enhanced by cooling the laser in liquid nitrogen (77 K). The polarization modes and linewidth of each wavelength are measured with high resolution by heterodyning with a local oscillator. The homogeneous linewidth of the Er³⁺:Yb ³⁺ fiber at 77 K is determined to be ~0.5 nm, from spectral-hole-burning measurements, which accounts for the generation of a stable multiwavelength lasing comb with wavelength separations of 0.4 nm     

Adaptive Variable Structure Control Design for Uncertain Time-Delayed Systems with Nonlinear Input

This paper presents a new robust control for uncertain dynamic time-delayed systems with series nonlinearities. It is implemented by using variable structure control. The proposed variable structure controller ensures the global reaching condition of the sliding mode of the uncertain time-delayed system. However, in the sliding mode, the investigated uncertain time-delayed system still bears the insensitivity to the uncertainties and disturbances as the systems with linear input. Furthermore, the proposed controller is achieved through adaptive variable structure control without the limitation of knowing the bounds of the uncertainties and perturbations in advance.     

Fast image retrieval using color-spatial information

In this paper, we present an image retrieval system that employs both the color and spatial information of images to facilitate the retrieval process. The basic unit used in our technique is a single-colored cluster, which bounds a homogeneous region of that color in an image. Two clusters from two images are similar if they are of the same color and overlap in the image space. The number of clusters that can be extracted from an image can be very large, and it affects the accuracy of retrieval. We study the effect of the number of clusters on retrieval effectiveness to determine an appropriate value for “optimal' performance. To facilitate efficient retrieval, we also propose a multi-tier indexing mechanism called the Sequenced Multi-Attribute Tree (SMAT). We implemented a two-tier SMAT, where the first layer is used to prune away clusters that are of different colors, while the second layer discriminates clusters of different spatial locality. We conducted an experimental study on an image database consisting of 12,000 images. Our results show the effectiveness of the proposed color-spatial approach, and the efficiency of the proposed indexing mechanism. Received August 1, 1997 / Accepted December 9, 1997     

Discovering Robust Knowledge from Databases that Change

Many applications of knowledge discovery and data mining such as rule discovery for semantic query optimization, database integration and decision support, require the knowledge to be consistent with the data. However, databases usually change over time and make machine-discovered knowledge inconsistent. Useful knowledge should be robust against database changes so that it is unlikely to become inconsistent after database updates. This paper defines this notion of robustness in the context of relational databases and describes how robustness of first-order Horn-clause rules can be estimated. Experimental results show that our estimation approach can accurately identify robust rules. We also present a rule antecedent pruning algorithm that improves the robustness and applicability of machine discovered rules to demonstrate the usefulness of robustness estimation.     

A basic-cycle calculation technique for efficient dynamic dataredistribution

Array redistribution is usually required to enhance algorithm performance in many parallel programs on distributed memory multicomputers. Since it is performed at run-time, there is a performance trade-off between the efficiency of the new data decomposition for a subsequent phase of an algorithm and the cost of redistributing data among processors. In this paper, we present a basic-cycle calculation technique to efficiently perform BLOCK-CYCLIC(S) to BLOCK-CYCLIC(t) redistribution. The main idea of the basic-cycle calculation technique is, first, to develop closed forms for computing source/destination processors of some specific array elements in a basic-cycle, which is defined as icm(s,t)/gcd(s,t). These closed forms are then used to efficiently determine the communication sets of a basic-cycle. From the source/destination processor/data sets of a basic-cycle, we can efficiently perform a BLOCK-CYCLIC(s) to BLOCK-CYCLIC(t) redistribution. To evaluate the performance of the basic-cycle calculation technique, we have implemented this technique on an IBM SP2 parallel machine, along with the PITFALLS method and the multiphase method. The cost models for these three methods are also presented. The experimental results show that the basic-cycle calculation technique outperforms the PITFALLS method and the multiphase method for most test samples     

Effects of heat treatment and testing temperature on fracture mechanics behavior of low-Si CA-15 stainless steel

This research studied the effects of heat treatment and testing temperature on fracture mechanics behavior of Si-modified CA-15 martensitic stainless steel (MSS), which is similar to AISI 403 grade stainless steel, which has been widely used in wall and blanket structures and in the pipe of nuclear power plant reactors, turbine blades, and nozzles. The results indicated that fracture toughness of low-Si CA-15 MSS is better than that of AISI 403. The specimens of the low-Si CA-15 MSS after austenitization at 1010 °C and then tempering at 300 °C have higher plane-strain fracture toughness (K _IC ) values for both 25 °C and −150 °C testing temperatures. However, the specimens tested at 150 °C cannot satisfy the plane-strain fracture toughness criteria. The fatigue crack growth rate is the slowest after austenitization at 1010 °C for 2 hours and tempering at 400 °C. Observing the crack propagation paths using a metallographic test, it was found that the cracking paths preferred orientation and branched along ferrite phase, owing to martensite-phase strengthening and grain-boundary-carbide retarding after 300 °C to 400 °C tempering. Also, transmission electron microscopy (TEM), scanning electron microscopy (SEM), and X-ray diffraction analysis were performed to correlate the properties attained to the microstructural observation.     

Mining sequential patterns by pattern-growth: the PrefixSpan approach

Sequential pattern mining is an important data mining problem with broad applications. However, it is also a difficult problem since the mining may have to generate or examine a combinatorially explosive number of intermediate subsequences. Most of the previously developed sequential pattern mining methods, such as GSP, explore a candidate generation-and-test approach [R. Agrawal et al. (1994)] to reduce the number of candidates to be examined. However, this approach may not be efficient in mining large sequence databases having numerous patterns and/or long patterns. In this paper, we propose a projection-based, sequential pattern-growth approach for efficient mining of sequential patterns. In this approach, a sequence database is recursively projected into a set of smaller projected databases, and sequential patterns are grown in each projected database by exploring only locally frequent fragments. Based on an initial study of the pattern growth-based sequential pattern mining, FreeSpan [J. Han et al. (2000)], we propose a more efficient method, called PSP, which offers ordered growth and reduced projected databases. To further improve the performance, a pseudoprojection technique is developed in PrefixSpan. A comprehensive performance study shows that PrefixSpan, in most cases, outperforms the a priori-based algorithm GSP, FreeSpan, and SPADE [M. Zaki, (2001)] (a sequential pattern mining algorithm that adopts vertical data format), and PrefixSpan integrated with pseudoprojection is the fastest among all the tested algorithms. Furthermore, this mining methodology can be extended to mining sequential patterns with user-specified constraints. The high promise of the pattern-growth approach may lead to its further extension toward efficient mining of other kinds of frequent patterns, such as frequent substructures.