33719933
Possibilistic C-Template Clustering and Its Application in Object Detection in Images
We present in this paper a new type of alternating-optimization based possibilistic c-shell clustering algorithm called possibilistic c-template (PCT). A template is represented by a set of line segments. A cluster prototype consists of a copy of the template after translation, scaling, and rotation transforms. This extends the capability of shell clustering beyond a few standard geometrical shapes that have been studied so far. We use a number of 2-dimensional data sets to illustrate the application of our algorithm in detecting generic template-based shapes in images. Techniques taken to relax the requirements of known number of clusters and good initialization are also described. Results for both synthetic and actual image data are presented.
15371718
Automatic Chromosome Classification Using Medial Axis Approximation and Band Profile Similarity
Automated chromosome classification is an essential task in cytogenetics and has been an important pattern recognition problem. Numerous attempts were made in the past to characterize chromosome to perform clinical and cancer cytogenetics research. It is important to determine optimum features and develop feature extraction schemes for chromosome classification. In this paper we propose novel approaches for medial axis determination and profile matching of human chromosome. The medial axis determination plays a critical role for precise and complete extraction of band patterns of chromosomes. The features of the band profile obtained along the axis are then used in the classification process. In particular, the medial axis is obtained by simple cross-section analysis and the classification is accomplished through iterative pairing of band profiles. According to the experimental results, the developed automatic system can efficiently extract band profiles of the chromosomes along their medial axes, and satisfactory chromosome classification results can be obtained.
42266951
TMO-structured cluster-based real-time management of location data on massive volume of moving items
A major challenge in the field of location based service (LBS) system engineering, is to establish a highly scalable system architecture which can be instantiated in moderate-size configurations handling thousands of moving items as well as in upper-end configurations handling millions of moving items. We are exploring an approach of using a cluster of database server nodes and effecting efficient distributed and parallel computing in both real-time update of location records of moving items and processing of location-related queries. The approach of developing efficient middleware which is layered on a cluster of nodes running commercial off-the-shelf database servers, has been adopted. The middleware design is substantially based on the TMO scheme for real-time distributed object programming and real-time distributed computing system design, which enables low-overhead coordination of distributed computations and highly abstract distributed programming styles.
18451647
Optimal circuit clustering for delay minimization under a more general delay model
This paper considers the area-constrained clustering of combinational circuits for delay minimization under a more general delay model, which practically takes variable interconnect delay into account. Our delay model is particularly applicable when allowing the back-annotation of actual delay information to drive the clustering process. We present a vertex grouping technique and integrate it with the algorithm (Rajaraman and Wong, 1995) such that our algorithm can be proved to solve the problem optimally in polynomial time.
10441616
Automatic Gait Optimization with Gaussian Process Regression
Gait optimization is a basic yet challenging problem for both quadrupedal and bipedal robots. Although techniques for automating the process exist, most involve local function optimization procedures that suffer from three key drawbacks. Local optimization techniques are naturally plagued by local optima, make no use of the expensive gait evaluations once a local step is taken, and do not explicitly model noise in gait evaluation. These drawbacks increase the need for a large number of gait evaluations, making optimization slow, data inefficient, and manually intensive. We present a Bayesian approach based on Gaussian process regression that addresses all three drawbacks. It uses a global search strategy based on a posterior model inferred from all of the individual noisy evaluations. We demonstrate the technique on a quadruped robot, using it to optimize two different criteria: speed and smoothness. We show in both cases our technique requires dramatically fewer gait evaluations than state-of-the-art local gradient approaches.
45880739
Optimal circuit clustering with variable interconnect delay
The paper aims at extending the circuit clustering algorithm of Rajaraman and Wong (1995) to handle a more sophisticated delay model, which practically takes variable interconnect delay into account. Our delay model is particularly applicable in allowing the back-annotation of actual delay information to drive the clustering process. We first show that the original algorithm fails to produce optimal solutions for this delay model. In order to solve the problem, a generalized algorithm based on an extension of Rajaraman and Wong is proposed such that the problem can be solved optimally while the polynomial time complexity is maintained.
14666453
An Interpolated Volume Data Model
Publisher Summary This chapter explores the basic functionality of the volume data model. It considers examples from biology and volumes of measurement from quantum physics. The data model is similar in the two cases, but the operations that are commonly used are quite different. In the second case, for instance, one is often interested in conditions on the behavior of local differential operators, such as zero-flow surfaces, while in the biological case one has a mix of value conditions and geometric condition. With these two application fields, this chapter highlights the generality and flexibility of the model. The system is based on a commercial database, augmented with specialized functions to manipulate the volume data model. The chapter demonstrates various operations specifying queries both using a graphical user interface and entering them directly in structured query language (SQL) augmented with the volume algebra operations.