Multinet : enabler for next generation enterprise wireless services

Wireless communications are currently experiencing a fast migration toward the beyond third-generation (B3G)/fourth generation (4G) era. This represents a generational change in wireless systems: new capabilities related to mobility and new services support is required and new concepts as individual-centric, user-centric or ambient-aware communications are included. One of the main restrictions associated to wireless technology is mobility management, this feature was not considered in the design phase; for this reason, a complete solution is not already found, although different solutions are proposed and are being proposed. In MULTINET project, features as mobility and multihoming are applied to wireless network to provide the necessary network and application functionality enhancements for seamless data communication mobility considering end-user scenario and preferences. The aim of this paper is to show the benefits of these functionalities from the Service Providers and final User point of view

COST292 experimental framework for TRECVID 2008

In this paper, we give an overview of the four tasks submitted to TRECVID 2008 by COST292. The high-level feature extraction framework comprises four systems. The first system transforms a set of low-level descriptors into the semantic space using Latent Semantic Analysis and utilises neural networks for feature detection. The second system uses a multi-modal classifier based on SVMs and several descriptors. The third system uses three image classifiers based on ant colony optimisation, particle swarm optimisation and a multi-objective learning algorithm. The fourth system uses a Gaussian model for singing detection and a person detection algorithm. The search task is based on an interactive retrieval application combining retrieval functionalities in various modalities with a user interface supporting automatic and interactive search over all queries submitted. The rushes task submission is based on a spectral clustering approach for removing similar scenes based on eigenvalues of frame similarity matrix and and a redundancy removal strategy which depends on semantic features extraction such as camera motion and faces. Finally, the submission to the copy detection task is conducted by two different systems. The first system consists of a video module and an audio module. The second system is based on mid-level features that are related to the temporal structure of videos

The COST292 experimental framework for TRECVID 2007

In this paper, we give an overview of the four tasks submitted to TRECVID 2007 by COST292. In shot boundary (SB) detection task, four SB detectors have been developed and the results are merged using two merging algorithms. The framework developed for the high-level feature extraction task comprises four systems. The first system transforms a set of low-level descriptors into the semantic space using Latent Semantic Analysis and utilises neural networks for feature detection. The second system uses a Bayesian classifier trained with a “bag of subregions”. The third system uses a multi-modal classifier based on SVMs and several descriptors. The fourth system uses two image classifiers based on ant colony optimisation and particle swarm optimisation respectively. The system submitted to the search task is an interactive retrieval application combining retrieval functionalities in various modalities with a user interface supporting automatic and interactive search over all queries submitted. Finally, the rushes task submission is based on a video summarisation and browsing system comprising two different interest curve algorithms and three features

The COST292 experimental framework for TRECVID 2007

In this paper, we give an overview of the four tasks submitted to TRECVID 2007 by COST292. In shot boundary (SB) detection task, four SB detectors have been developed and the results are merged using two merging algorithms. The framework developed for the high-level feature extraction task comprises four systems. The first system transforms a set of low-level descriptors into the semantic space using Latent Semantic Analysis and utilises neural networks for feature detection. The second system uses a Bayesian classifier trained with a "bag of subregions". The third system uses a multi-modal classifier based on SVMs and several descriptors. The fourth system uses two image classifiers based on ant colony optimisation and particle swarm optimisation respectively. The system submitted to the search task is an interactive retrieval application combining retrieval functionalities in various modalities with a user interface supporting automatic and interactive search over all queries submitted. Finally, the rushes task submission is based on a video summarisation and browsing system comprising two different interest curve algorithms and three features

QoS-aware network-supported architecture to distribute application flows over multiple network interfaces for B3G users

Users in the Beyond-Third-Generation (B3G) wireless system expect to receive ubiquitous communication services with customised quality-of-service (QoS) commitments for different applications, preferably in a way as transparent as possible. Ideally, flows belonging to diverse applications can be automatically and optimally distributed (or handed off) among the most appropriate access networks for multihomed users. To contribute to realising this vision, we propose a novel architecture to achieve QoS-aware policy-based flow handoffs for multihomed users, especially those equipped with more than a single personal device. In this architecture, advanced network intelligence enables a personal gateway to handle flow distributions dynamically for all the devices behind it according to the applications' QoS requirements and the current available network resources. The essential procedures in this architecture are described. Following that, the flow handoff delay is analysed and numerical results are illustrated. To prove the proposed concepts, up-to-date implementations with experimental results are also presented

MICHE Competitions: A Realistic Experience with Uncontrolled Eye Region Acquisition

People struggle every day with authentication to access a protected service or location, or simply aimed at protecting one’s own devices. This spurs a growing demand for self-handled authentication strategies. The increasing number of remote services of various kinds corresponds to an increasing number of passwords to use and remember, and also to the growth of the password theft risk, due to the increasing value of the protected resources. The other core element in present authentication scenarios is the ubiquity of mobile equipment. Smartphones add a “whatever” dimension to the possible uses of the mobile devices whenever and wherever that include storing/transferring multimedia information, often personal and often sensitive. Biometrics can both enforce and simplify authentication in controlled environments. Mobile biometrics in uncontrolled settings, where there is no operator to guide the capture of a “good-quality” sample on a mobile device, is the new frontier for secure use of data and services. The iris is among the best candidates for biometric recognition. It is extremely discriminative: Right and left irises of the same person are so different to hinder a correct matching, because randotypic elements largely overcome genotypic ones in individual development. However, self-acquired samples often suffer from poor quality, due, e.g., to reflections, motion blurring, out of focus, or bad image framing. Mobile setting and especially the inherent problems related to uncontrolled iris image acquisition are addressed in the two challenges of the MICHE project, whose results are the core topic of this chapter

COST292 experimental framework for TRECVID 2008

In this paper, we give an overview of the four tasks submitted to TRECVID 2008 by COST292. The high-level feature extraction framework comprises four systems. The first system transforms a set of low-level descriptors into the semantic space using Latent Semantic Analysis and utilises neural networks for feature detection. The second system uses a multi-modal classifier based on SVMs and several descriptors. The third system uses three image classifiers based on ant colony optimisation, particle swarm optimisation and a multi-objective learning algorithm. The fourth system uses a Gaussian model for singing detection and a person detection algorithm. The search task is based on an interactive retrieval application combining retrieval functionalities in various modalities with a user interface supporting automatic and interactive search over all queries submitted. The rushes task submission is based on a spectral clustering approach for removing similar scenes based on eigenvalues of frame similarity matrix and and a redundancy removal strategy which depends on semantic features extraction such as camera motion and faces. Finally, the submission to the copy detection task is conducted by two different systems. The first system consists of a video module and an audio module