A Variation on Hop-counting for Geographic Routing

Abstract: In this paper, we propose a new abstraction for localization — Hop Coordinates — that can improve the accuracy of geographic routing over that possible with a localization algorithm based only on hop counting. As compared with previous proposed localization algorithms, which require additional hardware or special antennas, our technique is similar to hop counting and only requires the connectivity of network. Using simulations, we show that incorporating our technique improves the accuracy of geographic routing in localization results generated by MDS-MAP and MDS-MAP(P) by 22 % and 14%, respectively. 1

Automatic Video Pause Detection Filter

Increasing interest in multimedia research has been drawn upon the development of video indexing and content-based image retrieval techniques. In this report, we proposed several pause detection algorithms, which instead of searching for significant visual transitions, the algorithms detect significant pauses in video streams. A realization of the algorithms was implemented using ImageTcl toolkit developed at Dartmouth Experimental Visualization Laboratory. In addition to proposing and studying the effectiveness of the pause detection algorithms, another major goal will be to incorporate our algorithms into ImageTcl and test the stability and applicability of the ImageTcl environment. Priliminary experiments showed relatively good results of our pause detection algorithms. 1 Introduction Recently, increasing interest in multimedia research has been drawn upon the development of video indexing and content-based image retrieval. Various schemes for automatic indexing of video databases ..

GDL: A Geographic Distributed Localization Algorithm for Wireless Sensor Networks

Abstract — Localization is a fundamental problem in sensor networks. This paper proposes a new distributed localization algorithm called GDL (“Geographic Distributed Localization”) that is based on hop-counting. Although the idea of predicting node position from connectivity information in WSNs is not new, our algorithm improves on other localization algorithms of this type in several key ways. Results from extensive simulation in NS-2 show a significant improvement in the accuracy with which locations can be determined in WSNs with varying density and different number of nodes on irregular placements. In particular, when compared with the MDS-MAP series of algorithms, our algorithm achieves about a 30–40 % improvement in accuracy. It also has a lower communication cost (O(n) versus O(nlogn)) than MDS-MAP(P), and has a low constant memory cost per node. Keywords-component; Position estimation, localization, sensor networks, hop counting, multidimensional scaling

PCS-TR97-307 Automatic Video Pause Detection Filter

Increasing interest in multimedia research has been drawn upon the development of video indexing and content-based image retrieval techniques. In this report, we proposed several pause detection algorithms, which instead of searching for signi cant visual transitions, the algorithms detect signi cant pauses in video streams. A realization of the algorithms was implemented using ImageTcl toolkit developed at Dartmouth Experimental Visualization Laboratory. In addition to proposing and studying the e ectiveness of the pause detection algorithms, another major goal will be to incorporate our algorithms into ImageTcl and test the stability and applicability of the ImageTcl environment. Priliminary experiments showed relatively good results of our pause detection algorithms.

Ontology-based Image Retrieval

Abstract:- The current most desirable image retrieval feature is retrieving images based on their semantic content. Currently there are two major image retrieval paradigms that attempt to provide this: text-based metadata image retrieval and content-based image retrieval. In practical applications, both have limitations. In this paper, we discuss an ontology-based image retrieval approach that aims to standardize image description and the understanding of semantic content. Ontology-based image retrieval has the potential to fully describe the semantic content of an image, allowing the similarity between images and retrieval query to be computed accurately

BP-Neural Network based- characterization of Electrographic Magnetohydrodynamic Signals in MR

Abstract—Electrocardiographic (ECG) signal collected during magnetic resonance (MR) imaging is affected by signal artifact because magnetic fields produce competing signals, from moving conductors in the large vessels. That is called the magnetohydrodynamic effect, which makes it difficult to recognize ST-T changes from ECG signal collected in a magnetic field (MRI). Resolving that problem is important both for accurate triggering (elimination of false triggers from tall peaked T waves) and for monitoring (identifying if or when patient develops ischemia or myocardial injury). This paper describes an algorithm based on neural network that is designed to cancel this artifact for ECG signal acquired during MR imaging. Keywords—Neural Network, ECG, magnetohydrodynamic effect, aorta model, Source separatio

S.: Similarity searches in heterogeneous feature spaces

Abstract: Correlating event streams or development paths of observed behavior that involves disparate types of data is a common problem in many applications including biomedical and clinical diagnosis systems. We present a new formulation of the following dual problem: (a) given multiple event streams for which we have prior knowledge, specify a feature space with heterogeneous dissimilarity measures, and (b) find similar time series given these (expert) user-specified heterogeneities, both within the same feature and as combinations across multiple features. By allowing domain experts to describe their feature spaces (quantized representation of observations such as the size of an object, its primary axis, its shape, etc.) more accurately in this fashion, query matches are better suited to the domain experts ’ needs. The presented work augments the existing research of finding local similarity areas and overall patterns in time series data. Key-Words: database queries, dissimilarity measures, prior knowledge

An Extensible Framework for Sharing Clinical Guidelines and Services

Abstract — Accurate and descriptive information from clinical studies guides improvements in health care. Clinical guidelines established by authoritative medical organizations provide such information in a standard form for medical professionals’ reference. Previous work on electronically sharing clinical guidelines focuses on the idea of building unified clinical terminologies and sharing resources through centralized data repositories. In this paper we propose a novel five-layer framework called the Extensible Clinical Guidelines and Services Sharing Architecture (ECGSSA). This framework provides for clinical guideline sharing among autonomous service providers over a distributed architecture. Requests for exchange of guidelines are disseminated through Web Services through a registry mechanism. Currently we have adopted the Guideline Interchange Format (GLIF) from InterMed as the representation format and use the Open Grid Services Architecture (OGSA) to attain virtual organization of shared guideline and service resources. This approach will allow more flexibility for medical professionals to exchange their practice guidelines in an effort to improve quality of health care. Also, it extends the possibility of solving clinic-related computational problems through collaborative sharing of analytical services. A sample scenario is presented to explain the application of ECGSSA in distributed task assignment and service matching in medical image processing