Abstract
Regular services in telecommunications produce massive volumes of relational data. In this work the data produced in telecommunications is seen as a streaming network, where clients are the nodes and phone calls are the edges. Visualization techniques are required for exploratory data analysis and event detection. In social network visualization and analysis the goal is to get more information from the data taking into account actors at the individual level. Previous methods relied on aggregating communities, k-Core decompositions and matrix feature representations to visualize and analyse the massive network data. Our contribution is a group visualization and analysis technique of influential actors in the network by sampling the full network with a top-k representation of the network data stream. © Springer International Publishing 2015.

Abstract

Abstract
This paper explores an innovative approach to model Lead-Acid battery energy storage systems (BESS) in insular power grid applications. In this context, two insular power networks are employed as real case studies. A critical analysis with regards to some BESS design parameters is performed. Finally, a charging sensibility analysis based criterion is proposed with a view to extend BESS life time.

Abstract
The dance music genres of hardcore, jungle and drum and bass (HJDB) emerged in the United Kingdom during the early 1990s as a result of affordable consumer sampling technology and the popularity of rave music and culture. A key attribute of these genres is their usage of fast-paced drums known as breakbeats. Automated analysis of breakbeat usage in HJDB would allow for novel digital audio effects and musicological investigation of the genres. An obstacle in this regard is the automated identification of breakbeats used in HJDB music. This paper compares three strategies for breakbeat detection: (1) a generalised frame-based music classification scheme; (2) a specialised system that segments drums from the audio signal and labels them with an SVM classifier; (3) an alternative specialised approach using a deep network classifier. The results of our evaluations demonstrate the superiority of the specialised approaches, and highlight the need for style-specific workflows in the determination of particular musical attributes in idiosyncratic genres. We then leverage the output of the breakbeat classification system to produce an automated breakbeat sequence reconstruction, ultimately recreating the HJDB percussion arrangement.

Abstract
In this paper, we adopt a novel numerical algorithm, referred to as dual augmented Lagrangian method (DALM), for efficient test cost reduction based on spatial variation modeling. The key idea of DALM is to derive the dual formulation of the L-1-regularized least-squares problem posed by Virtual Probe (VP), which can be efficiently solved with substantially lower computational cost than its primal formulation. In addition, a number of unique properties associated with discrete cosine transform (DCT) are exploited to further reduce the computational cost of DALM. Our experimental results of an industrial RF transceiver demonstrate that the proposed DALM solver achieves up to 38 runtime speed-up over the conventional interior-point solver without sacrificing any performance on escape rate and yield loss for test applications.

Abstract
Narrow band parametric speech coding and wideband audio coding represent opposite coding paradigms involving audible information, namely in terms of the specificity of the audio material, target bit rates, audio quality and application scenarios. In this paper we explore a new avenue addressing parametric coding of wideband speech, using the potential and accuracy provided by frequency-domain signal analysis and modeling techniques that typically belong to the realm of high-quality audio coding. A first analysis-synthesis validation framework is described that illustrates the decomposition, parametric representation and synthesis of perceptually and linguistically relevant speech components while preserving naturalness and speaker specific information.