26820236
3-D IFS fractals as real-time graphics model
Abstract Fractals have recently been attracting increasing research interest. Unlike conventional geometrical figures, fractals have unlimited details—a potentially useful feature for artistic images. One way to generate fractals is to use Iterated Function Systems (IFS). There have been significant research efforts on 2-D IFS. Programs for rendering 2-D IFS fractals are readily available. 3-D IFS appear to be a natural extension but remain an area to be investigated. This paper explores the use of 3-D IFS for creating fascinating fractal scenes. An efficient coloring, lighting and mist effects scheme is proposed. A program using the proposed scheme has been developed that can render delicate scenes consisting of multiple IFS fractals in real-time on the PC.
8311095
Classifying image texture with statistical landscape features
This paper proposes to use three-dimensional information derived from the graph of an image function for texture description. The graph of an image function is a rumpled surface appearing like a landscape. To characterize the texture through this landscape, six novel texture feature curves based on the statistics of the geometrical and topological properties of the solids shaped by the graph and a variable horizontal plane are used. The proposed statistical landscape features have been shown by systematic experiments to offer very low error rates on a large subset of the Brodatz texture album having excluded some nonhomogeneous images, the entire Brodatz texture set, as well as the VisTex texture collection.
16583184
Using cluster skeleton as prototype for data labeling
A new approach, designed for clustering data whose underlying distribution shapes are arbitrary, is presented. This study is concerned with the use of the skeleton of a cluster as its prototype, which can represent the cluster more closely than that of using a single data point. The given data set is then partitioned into those skeleton-represented clusters without any prior knowledge nor assumptions of hidden structures. A novel function called cluster characteristic function (CCF) has been constructed and the associated theorems have been proposed and proved that the proper number of clusters can be determined with the approach.
16801553
Mini-Max Initialization for Function Approximation
No abstract available.
45083530
Improving backpropagation learning under limited precision
Abstract Most of the work to date on learning in neural networks assumes the availability of unlimited precision for implementing weights and the neural transfer functions. Recently some studies have investigated the effects of limited precision on the learning and generalisation performance. In this paper, we identify the causes of performance degradation due to limited precision and propose a novel algorithm to improve the performance of Backpropagation.
205904713
Fuzzy aggregated connectedness for image segmentation
Fuzzy methodology has been successfully applied to image analysis and pattern recognition since the theory of fuzzy set was 0rst introduced by Zadeh [1]. It is believed that the fuzzy set theory models the inherent imprecision in images. Fuzzy geometrical approaches focus on the spatial measures and have attracted considerable research interest in handling the spatial uncertainty in images [2]. Fuzzy connectedness is an important topological property of images. The classical de0nition was given by Rosenfeld [3]. A modi0cation to the traditional concept of fuzzy connectedness was further proposed [4]. Based on the notion of fuzzy connectedness of image elements, fuzzy objects for n-dimensional digital spaces was exploited for image segmentation [5]. Fuzzy connectedness is a natural extension to the crisp connectedness and re:ects the imprecise topological structures of images. The algorithms in the literature usually compute the fuzzy connectedness with regard to the best path between a pair of points. This approach su;ers from high sensitivity to noise. Noise in corrupted images may misguide the detection of the best path and further in:uence the determination of fuzzy connectedness. We believe that this has mainly been due to the binarization in determining the best path. The information of a path is either fully preserved or discarded and this contradicts the concept of fuzzy sets. In this paper,
14356634
A service scheduler in a trustworthy system
The aim of the research is to investigate techniques that support efficient service scheduling algorithms in a service-oriented fault-tolerant real-time distributed system. Techniques we developed include deadline-based real-time scheduling, priority-based scheduling, and redundant resource allocation for fault-tolerance. The system model and scheduling algorithms are designed, and a prototype is implemented to facilitate the investigation and experimentation.
10882838
A multivariate prediction model for microarray cross-hybridization
BackgroundExpression microarray analysis is one of the most popular molecular diagnostic techniques in the post-genomic era. However, this technique faces the fundamental problem of potential cross-hybridization. This is a pervasive problem for both oligonucleotide and cDNA microarrays; it is considered particularly problematic for the latter. No comprehensive multivariate predictive modeling has been performed to understand how multiple variables contribute to (cross-) hybridization.ResultsWe propose a systematic search strategy using multiple multivariate models [multiple linear regressions, regression trees, and artificial neural network analyses (ANNs)] to select an effective set of predictors for hybridization. We validate this approach on a set of DNA microarrays with cytochrome p450 family genes. The performance of our multiple multivariate models is compared with that of a recently proposed third-order polynomial regression method that uses percent identity as the sole predictor. All multivariate models agree that the 'most contiguous base pairs between probe and target sequences,' rather than percent identity, is the best univariate predictor. The predictive power is improved by inclusion of additional nonlinear effects, in particular target GC content, when regression trees or ANNs are used.ConclusionA systematic multivariate approach is provided to assess the importance of multiple sequence features for hybridization and of relationships among these features. This approach can easily be applied to larger datasets. This will allow future developments of generalized hybridization models that will be able to correct for false-positive cross-hybridization signals in expression experiments.
14597615
Priority assignment optimization for minimization of current surge in high performance power efficient clock-gated microprocessor
We propose an integrated architectural/physical-planning approach named priority assignment optimization to minimize the current surge in high performance power efficient clock-gated microprocessors. The proposed approach balances the current demands across the floorplan by assigning optimized priorities to the functional units (FUs). Two complementary methods -- physical planning with soft modules and issue pattern management - to enhance our proposed approach are also discussed for various applications. Experimental results show that the proposed approach reduces the peak noise by 11.75% and consequently, the decoupling capacitance (Decap) requirement by 24.22% without any degradation in IPC (Instruction Per Cycle). We also show that our approach does not increase the clock period for the 0.18μm technology and beyond.
17883721
Classification of self-consumable highlights for soccer video summaries
An effective scheme for soccer summarization is significant to improve the usage of this massively growing video data. The paper presents an extension to our recent work which proposed a framework to integrate highlights into play-breaks to construct more complete soccer summaries. The current focus is to demonstrate the benefits of detecting some specific audio-visual features during play-break sequences in order to classify highlights contained within them. The main purpose is to generate summaries which are self-consumable individually. To support this framework, the algorithms for shot classification and detection of near-goal and slow-motion replay scenes is described. The results of our experiment using 5 soccer videos (20 minutes each) show the performance and reliability of our framework
894881
Performance improvement of TCP Vegas over heterogeneous networks
Current IP network has become the dominant paradigm for all networking environments. The significant cause of packet losses in such heterogenous networks is no longer limited to network congestion. Traditional TCP interprets every packet loss as caused by congestion which may be not the case in the current Internet. Misinterpretation of wireless random loss as an indication of network congestion results in TCP slowing down its sending rate unnecessarily. We propose a new variant of TCP Vegas named RedVegas. By using the innate nature of Vegas and congestion indications marked by routers, RedVegas may detect random packet losses precisely. Through the packet loss differentiation, RedVegas reacts appropriately to the losses, and therefore the throughput of connection over heterogeneous networks can be significantly improved.