Ericsson Nikola Tesla

About: Ericsson Nikola Tesla is a based out in . It is known for research contribution in the topics: The Internet & Next-generation network. The organization has 271 authors who have published 370 publications receiving 2419 citations.

A Second Replicated Quantitative Analysis of Fault Distributions in Complex Software Systems

Abstract: To contribute to the body of empirical research on fault distributions during development of complex software systems, a replication of a study of Fenton and Ohlsson is conducted. The hypotheses from the original study are investigated using data taken from an environment that differs in terms of system size, project duration, and programming language. We have investigated four sets of hypotheses on data from three successive telecommunications projects: 1) the Pareto principle, that is, a small number of modules contain a majority of the faults (in the replication, the Pareto principle is confirmed), 2) fault persistence between test phases (a high fault incidence in function testing is shown to imply the same in system testing, as well as prerelease versus postrelease fault incidence), 3) the relation between number of faults and lines of code (the size relation from the original study could be neither confirmed nor disproved in the replication), and 4) fault density similarities across test phases and projects (in the replication study, fault densities are confirmed to be similar across projects). Through this replication study, we have contributed to what is known on fault distributions, which seem to be stable across environments.

VCL@FER Image Quality Assessment Database

Abstract: In this paper we present new image quality database VCL@FER (http://www.vcl.fer.hr/quality/) which consists of four degradation types, 6 levels of each degradation and 23 different images (552 degraded images). It can be used in objective image quality evaluation, as well as to develop and test new image quality measures. Results for six commonly used full reference objective quality measures are compared using newly developed image database, as well as 6 other image databases.

Application-Level QoS Negotiation and Signaling for Advanced Multimedia Services in the IMS

Abstract: The IP multimedia subsystem (IMS) has been recognized as a reference next-generation network architecture for offering multimedia services over an Internet Protocol (IP)-based infrastructure. One of the key benefits of the IMS is efficient and flexible introduction of new services and access to third-party application providers, thanks to standard interfaces and standardized service capabilities. To support novel media-rich applications across a wide range of user devices and access networks, IMS must support negotiable quality of service (QoS) for IP multimedia sessions. In this article, we describe the application-level QoS signaling as specified by the 3GPP and propose some enhancements based on advanced QoS parameter matching and optimization functionality to be included along the signaling path. We outline various signaling flow scenarios and discuss them in the context of a case study involving an IMS-supported 3D virtual environment, featuring a treasure-hunt-like game.

Spatio-temporal image segmentation using optical flow and clustering algorithm

Abstract: Image segmentation is an important and challenging problem in image analysis. Segmentation of moving objects in image sequences is even more difficult and computationally expensive. In this work we propose a technique for spatio-temporal segmentation of medical image sequences based on clustering in the feature vector space. The motivation for the spatio-temporal approach is the fact that motion is a useful clue for object segmentation. A two-dimensional feature vector has been used for clustering in the feature space. The first feature is image brightness which reveals the structure of interest in the image. The second feature is the Euclidean norm of the optical flow vector. The optical flow field is computed using a Horn-Schunck algorithm. By clustering in the feature space, it is possible to detect a moving object in the image. Experiments have been conducted using a sequence of ECG-gated magnetic resonance (MR) images of a beating heart. The method is also tested on images with moving background. The experiments have shown encouraging results.