6695051
Ontology Driven Concept Approximation
This paper investigates the concept approximation problem using ontology as an domain knowledge representation model and rough set theory. In [7] [8], we have presented a rough set based multi-layered learning framework for approximation of complex concepts assuming the existence of a simple concept hierarchy. The proposed methodology utilizes the ontology structure to learn compound concepts using the rough approximations of the primitive concepts as input attributes. In this paper we consider the extended model for knowledge representation where the concept hierarchies are embedded with additional knowledge in a form of relations or constrains among sub-concepts. We present an extended multi-layered learning scheme that can incorporate the additional knowledge and propose some classes of such relations that assure an improvement of the learning algorithm as well as a convenience of the knowledge modeling process. We illustrate the proposed method and present some results of experiment with data from sunspot recognition problem.
11160591
Learning Sunspot Classification
This paper describes automatic sunspot recognition and classification from satellite images. Some experimental results on learning sunspot classification using data mining techniques are presented. The classification scheme used was the seven-class Modified Zurich scheme. Daily images of the solar disk were taken from the NASA SOHO satellite’s MDI instrument and sunspots extracted using image processing techniques. Two data mining tools, WEKA and RSES, were used for learning sunspot classification. In the training dataset sunspots were manually classified by comparing extracted sunspots with corresponding active region maps.
37264651
Rough Set Approach to Sunspot Classification Problem
This paper presents an application of hierarchical learning method based rough set theory to the problem of sunspot classification from satellite images. The Modified Zurich classification scheme [3] is defined by a set of rules containing many complicated and unprecise concepts, which cannot be determined directly from solar images. The idea is to represent the domain knowledge by an ontology of concepts – a treelike structure that describes the relationship between the target concepts, intermediate concepts and attributes. We show that such ontology can be constructed by a decision tree algorithm and demonstrate the proposed method on the data set containing sunspot extracted from satellite images of solar disk.
35277097
Maximum-likelihood parameter estimation for image ringing-artifact removal
At low bit rates, image compression codecs based on overlapping transforms introduce spurious oscillations known as ringing artifacts in the vicinity of major edges. Unlike previous works, we present a maximum-likelihood approach to the ringing-artifact removal problem. Our approach employs a parameter estimation method based on the k-means algorithm with the number of clusters determined by a cluster-separation measure. The proposed algorithm and its simplified approximation are applied to JPEG2000 compressed images. Our results show effective and efficient removal of ringing artifacts.
1402747
'SemanticLIFE' - A Framework for Managing Information of A Human Lifetime
The SemanticLIFE' system is designed to store, manage and retrieve ones lifetime’s information entities. It enables the acquisition and storage of data while giving annotations to email messages, browsed web pages, phone calls, images, contacts, life events and other resources. 'SemanticLIFE' 
also provides intuitive and effective search mechanism based upon stored semantics. This paper presents the framework for the system to devise lifetime data store and search using recognized standards. We are aiming at a system which supports the long term memory by associating metadata with content and ontologies. The ultimate goal of our project is to build a Personal Information Management system over a Human Lifetime using ontologies as a basis for the representation of its content.
17197783
Multiple sender distributed video streaming
With the explosive growth of video applications over the Internet, many approaches have been proposed to stream video effectively over packet switched, best-effort networks. We propose a receiver-driven protocol for simultaneous video streaming from multiple senders to a single receiver in order to achieve higher throughput, and to increase tolerance to packet loss and delay due to network congestion. Our receiver-driven protocol employs a novel rate allocation algorithm (RAA) and a packet partition algorithm (PPA). The RAA, run at the receiver, determines the sending rate for each sender by taking into account available network bandwidth, channel characteristics, and a prespecified, fixed level of forward error correction, in such a way as to minimize the probability of packet loss. The PPA, run at the senders based on a set of parameters estimated by the receiver, ensures that every packet is sent by one and only one sender, and at the same time, minimizes the startup delay. Using both simulations and Internet experiments, we demonstrate the effectiveness of our protocol in reducing packet loss.
18341353
Alias verification for Fortran code optimization
Abstract Alias analysis for Fortran is less complicated than for programming languages with pointers but many real Fortran programs violate the standard: a formal parameter or a common variable that is aliased with another formal parameter is modified. Compilers, assuming standard-conforming programs, consider that an assignment to one variable will not change the value of any other variable, allowing optimizations involving the aliased variables. Higher performance results but anything may happen: the program may appear to run normally, or may produce incorrect answers, or may behave unpredictably. The results may depend on the compiler and the optimization level. To guarantee the standard conformance of programs and to maintain the referential transparency in order to make program analyses exact and program optimizations safe, precise alias information retrieval at a reasonable cost, especially the determination of overlaps between arrays are studied in this paper. Static analyses and code instrumentation are used to find all violations of the prohibitions against aliasing in Fortran code. Alias violation tests are inserted only at places where it cannot be proved statically that they are useless in order to reduce the number of dynamic checks at run-time. A specific memory location naming technique is used to compact representation and enhance the precision of alias analysis. Modifications on the dependence graph created by aliasing are also studied to show the impact of aliases on some program optimizing transformations. Experimental results on SPEC95 benchmark are presented and some related issues are also discussed.
2066487
Maximum likelihood parameter estimation for image ringing artifact removal
At low bit rates, image compression codecs based on overlapping transforms introduce spurious oscillation known as ringing artifacts in the vicinity of major edges. The image quality can be enhanced considerably by removing the artifacts. We present a maximum likelihood approach to the ringing artifact removal problem. Our approach employs a parameter estimation method based on the k-means algorithm with the number of clusters determined by a cluster separation measure. The proposed algorithm and its simplified approximation are applied to JPEG2000 compressed images to demonstrate their effectiveness.