Recent advances in microarray technology allow scientists to measure expression levels of thousands of genes simultaneously in human tissue samples. This technology has been increasingly used in cancer research becaus...
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Recent advances in microarray technology allow scientists to measure expression levels of thousands of genes simultaneously in human tissue samples. This technology has been increasingly used in cancer research because of its potential for classification of the tissue samples based only on gene expression levels. A major problem in these microarray data is that the number of genes greatly exceeds the number of tissue samples. Moreover, these data have a noisy nature. It has been shown from literature review that selecting a small subset of informative genes can lead to an improved classification accuracy. Thus, this paper aims to select a small subset of informative genes that is most relevant for the cancer classification. To achieve this aim, an approach using two hybrid methods has been proposed. This approach is assessed on two well-known microarray data. The experimental results have shown that the gene subsets are very small in size and yield better classification accuracy as compared with other previous works as well as four methods experimented in this work. In addition, a list of informative genes in the best subsets is also presented for biological usage.
We build on PTIDES, a programming model for distributed embedded systems that uses discrete-event (DE) models as program specifications. PTIDES improves on distributed DE execution by allowing more concurrent event pr...
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We build on PTIDES, a programming model for distributed embedded systems that uses discrete-event (DE) models as program specifications. PTIDES improves on distributed DE execution by allowing more concurrent event processing without backtracking. This paper discusses the general execution strategy for PTIDES, and provides two feasible implementations. This execution strategy is then extended with tolerance for hardware errors. We take a program transformation approach to automatically enhance DE models with incremental checkpointing and state recovery functionality. Our fault tolerance mechanism is lightweight and has low overhead. It requires very little human intervention. We incorporate this mechanism into PTIDES for efficient execution of fault- tolerant real-time distributed DE systems.
Sensor networks employed by scientific applications oftenneed to support localized collaboration of sensor nodes to perform in-network data processing. This includes new quantitative synthesis andhypothesis testing in...
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ISBN:
(纸本)9783540898931
Sensor networks employed by scientific applications oftenneed to support localized collaboration of sensor nodes to perform in-network data processing. This includes new quantitative synthesis andhypothesis testing in near real time, as data streaming from distributedinstruments, to transform raw data into high level domain-dependent information. This paper investigates in-network data processing mechanismswith dynamic data requirements in resource constrained heterogeneoussensor networks. Particularly, we explore how the temporaland spatial correlation of sensor measurements can be used to trade offbetween the complexity of coordination among sensor clusters and thesavings that result from having fewer sensors involved in in-network processing,while maintaining an acceptable error threshold. Experimentalresults show that the proposed in-network mechanisms can facilitate theefficient usage of resources and satisfy data requirement in the presenceof dynamics and uncertainty.
Target tracking and localization are important applications in camera sensor networks. Although coverage is a very important research topic in wireless sensor networks, and the coverage problem for target detection ha...
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This paper presents an integrated decision support system for an automated melanoma recognition of dermoscopic images based on multiple expert fusion. In this context, the ultimate aim is to support decision making by...
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This paper presents an integrated decision support system for an automated melanoma recognition of dermoscopic images based on multiple expert fusion. In this context, the ultimate aim is to support decision making by predicting image categories (e.g., melanoma, benign and dysplastic nevi) by combining outputs from different classifiers. A fast and automatic segmentation method to detect the lesion from the background healthy skin is proposed and lesion-specific local color and texture-related features are extracted. For the classification, combining experts which are classifiers with different structures, are examined as alternative solution instead of an individual classifier. In this approach, probabilistic outputs of the experts are combined based on the combination rules that are derived by following Bayespsila theorem. The category label with the highest confidence score is considered to be the class of a test image. Experimental results on a collection of 358 dermoscopic images demonstrate the effectiveness of the proposed expert fusion-based approach.
This paper is concerned with prototype development and motion control of a dolphin-like underwater robot. The propulsion and maneuvering of the robotic dolphin are realized with the flapping motion of the mechanical f...
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This paper is concerned with prototype development and motion control of a dolphin-like underwater robot. The propulsion and maneuvering of the robotic dolphin are realized with the flapping motion of the mechanical flippers and the combined heaving and pitching motions of the fluke. Mechanical design and control of the flipper apparatus and the flexible tail mechanism are presented. Through coordinated control of the propulsors, several swimming movements are designed. Preliminary experimental results verify the effectiveness of the proposed design scheme.
A content delivery network (CDN) is expected to provide high performance content delivery, which requires scalable infrastructure to achieve global coverage. The provision of such infrastructure may form a substantial...
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A content delivery network (CDN) is expected to provide high performance content delivery, which requires scalable infrastructure to achieve global coverage. The provision of such infrastructure may form a substantial entry barrier for new CDN providers, as well as affecting commercial viability of the existing ones. Peering of CDNs can be a way to allow dynamic infrastructural cooperation between CDNs in a scalable manner, in order to mitigate the impact of flash crowds and to achieve better overall service times. In this paper, we present a quality of service (QoS)-driven model to evaluate the user perceived performance of CDN peering relationships. In this model, an overloaded CDN redirects a fraction of its incoming requests to peered CDNs and thereby can avoid the impact of flash crowds. The model-based approach also assists in making concrete QoS guarantee for a given CDN. Our approach endeavors to achieve scalability for a CDN in a user transparent manner.
We model the Sum and Product Riddle inpublic announcement logic, which is interpreted on an epistemic Kripke model. The model is symbolically represented as a finite state program with n agents. A model checking metho...
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Framework-based software development has been proven a useful technique to develop an application. However, the development of a large application framework itself is considered complex. There are two major causes for...
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Framework-based software development has been proven a useful technique to develop an application. However, the development of a large application framework itself is considered complex. There are two major causes for this problem, the size of a framework and the vague framework requirements. The idea of framelet a small but complete framework, can be applied to solve the former. The framelet-based approach for framework development (FAFD) has been proposed to address the later. There are some improvements in term of use case requirements description to the approach after it was first presented. This paper aims to report such improvements as well as the ongoing researches on the FAFD.
Component-based softwareengineering (CBSE) is becoming popular due to the benefits of software reuse and the availability of many alternatives of reusable components. By reusing existing software components that have...
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Component-based softwareengineering (CBSE) is becoming popular due to the benefits of software reuse and the availability of many alternatives of reusable components. By reusing existing software components that have already been tested, software engineers may reduce errors and shorten the time to market the system under development. However, software engineers still have a problem in selecting the existing reusable components as well as difficulties in determining the quality of the developed components for future reuse. Therefore, an appropriate component evaluation framework is extremely needed to harvest the full benefits of software reuse. In this paper, we present the comparative evaluation of the state-of-the-art component evaluation approaches with an emphasize on reusability. The results presented in this paper are very useful in achieving the ultimate aim of our research i.e. providing a holistic component evaluation approach that may assist software engineer both in the selection of the existing reusable components and the development of new reusable components.
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