Fast intra prediction algorithm based on texture analysis for 3D-HEVC encoders

The advances in display technologies and the growing popularity of 3D video systems have attracted more consumers for 3D viewing experiences, and, consequently, the demand for storage and transmission of 3D video content is increasing. To cope with this demand, a 3D video extension of high-efficiency video coding (HEVC) standard is being developed and near the final standardization stage. The upcoming 3D-HEVC standard is expected to provide higher encoding efficiency than its predecessors, supporting multiple views with high resolution, at a cost of considerable increase in computational complexity, which can be an obstacle to its use in real-time applications. This article proposes a novel complexity reduction algorithm developed to optimize the 3D-HEVC intra mode decision targeting real-time video processing for consumer devices with limited computational power, such as 3D camcorders and smartphones equipped with multiple cameras and depth acquisition capabilities. The proposed algorithm analyzes the texture frames and depth maps to estimate the orientation of edges present in the prediction unit data, speeding up the intra prediction process and reducing the 3D-HEVC encoding processing time. Experimental results demonstrate that the proposed algorithm can save 26 % in computational complexity on average with negligible loss of encoding efficiency. This solution contributes to make more feasible the compression of 3D videos targeting real-time applications in power-constrained devices.     

Linking the proteins--elucidation of proteome-scale networks using mass spectrometry

Proteomes are intricate. Typically, thousands of proteins interact through physical association and post-translational modifications (PTMs) to give rise to the emergent functions of cells. Understanding these functions requires one to study proteomes as "systems" rather than collections of individual protein molecules. The abstraction of the interacting proteome to "protein networks" has recently gained much attention, as networks are effective representations, that lose specific molecular details, but provide the ability to see the proteome as a whole. Mostly two aspects of the proteome have been represented by network models: proteome-wide physical protein-protein-binding interactions organized into Protein Interaction Networks (PINs), and proteome-wide PTM relations organized into Protein Signaling Networks (PSNs). Mass spectrometry (MS) techniques have been shown to be essential to reveal both of these aspects on a proteome-wide scale. Techniques such as affinity purification followed by MS have been used to elucidate protein-protein interactions, and MS-based quantitative phosphoproteomics is critical to understand the structure and dynamics of signaling through the proteome. We here review the current state-of-the-art MS-based analytical pipelines for the purpose to characterize proteome-scale networks.     

Modeling collaboration protocols for collaborative modeling tools: Experiences and applications

Over the last two decades, Collaborative Systems have become increasingly popular thanks to the many advances made in networks, communications and software tools. Within this field, Collaborative Modeling Systems apply the collaborative paradigm to the construction of (often visual) models, where users build diagrams from building blocks and the relationships between them. In these kinds of applications, the work is usually arranged into sessions, with the definition of some kind of time organization between those sessions. This organization is known as a collaboration protocol. Unfortunately, it is not usually easy to define these protocols, and many applications do not allow users to make any use of them.In an effort to overcome these difficulties, in this paper we propose a visual language for defining collaboration protocols for these systems. As such, in our language, sessions, artifacts and the transformations between them can be specified visually, and different coordination relationships (such as fork and join) can be defined. The visual language is included in a development method for collaborative systems that take advantage of the Eclipse platform in order to develop model-driven graphical editors that are enhanced with collaboration capabilities.     

HPC on the Grid: The Theophys Experience

The Grid Virtual Organization (VO) “Theophys”, associated to the INFN (Istituto Nazionale di Fisica Nucleare), is a theoretical physics community with various computational demands, spreading from serial, SMP, MPI and hybrid jobs. That has led, in the past 20 years, towards the use of the Grid infrastructure for serial jobs, while the execution of multi-threaded, MPI and hybrid jobs has been performed in several small-medium size clusters installed in different sites, with access through standard local submission methods. This work analyzes the support for parallel jobs in the scientific Grid middlewares, then describes how the community unified the management of most of its computational need (serial and parallel ones) using the Grid through the development of a specific project which integrates serial e parallel resources in a common Grid based framework. A centralized national cluster is deployed inside this framework, providing “Wholenodes” reservations, CPU affinity, and other new features supporting our High Performance Computing (HPC) applications in the Grid environment. Examples of the cluster performance for relevant parallel applications in theoretical physics are reported, focusing on the different kinds of parallel jobs that can be served by the new features introduced in the Grid.     

Experience with model-based performance, reliability, and adaptability assessment of a complex industrial architecture

In this paper, we report on our experience with the application of validated models to assess performance, reliability, and adaptability of a complex mission critical system that is being developed to dynamically monitor and control the position of an oil-drilling platform. We present real-time modeling results that show that all tasks are schedulable. We performed stochastic analysis of the distribution of task execution time as a function of the number of system interfaces. We report on the variability of task execution times for the expected system configurations. In addition, we have executed a system library for an important task inside the performance model simulator. We report on the measured algorithm convergence as a function of the number of vessel thrusters. We have also studied the system architecture adaptability by comparing the documented system architecture and the implemented source code. We report on the adaptability findings and the recommendations we were able to provide to the system’s architect. Finally, we have developed models of hardware and software reliability. We report on hardware and software reliability results based on the evaluation of the system architecture.     

A Web services-based framework for building componentized digital libraries

We present a new Web services-based framework for building componentized digital libraries (DLs). We particularly demonstrate how traditional RDBMS technology can be easily deployed to support several common digital library services. Configuration and customization of the framework to build specialized systems is supported by a wizard-like tool which is based on a generic metamodel for DLs. Such a tool implements a workflow process that segments the DL design tasks into well-defined steps and drives the designer along these steps. Both the framework and the configuration tool are evaluated in terms of several performance and usability criteria. Our experimental evaluation demonstrates the feasibility and superior performance of our framework, as well as the effectiveness of the wizard tool for setting up DLs.     

Motif patterns in 2D

Motif patterns consisting of sequences of intermixed solid and don’t-care characters have been introduced and studied in connection with pattern discovery problems of computational biology and other domains. In order to alleviate the exponential growth of such motifs, notions of maximal saturation and irredundancy have been formulated, whereby more or less compact subsets of the set of all motifs can be extracted, that are capable of expressing all others by suitable combinations. In this paper, we introduce the notion of maximal irredundant motifs in a two-dimensional array and develop initial properties and a combinatorial argument that poses a linear bound on the total number of such motifs. The remainder of the paper presents approaches to the discovery of irredundant motifs both by offline and incremental algorithms.     

Two proposals for the inclusion of directory information in the last-level private caches of glueless shared-memory multiprocessors

In glueless shared-memory multiprocessors where cache coherence is usually maintained using a directory-based protocol, the fast access to the on-chip components (caches and network router, among others) contrasts with the much slower main memory. Unfortunately, directory-based protocols need to obtain the sharing status of every memory block before coherence actions can be performed. This information has traditionally been stored in main memory, and therefore these cache coherence protocols are far from being optimal. In this work, we propose two alternative designs for the last-level private cache of glueless shared-memory multiprocessors: the lightweight directory and the SGluM cache. Our proposals completely remove directory information from main memory and store it in the home node’s L2 cache, thus reducing both the number of accesses to main memory and the directory memory overhead. The main characteristics of the lightweight directory are its simplicity and the significant improvement in the execution time for most applications. Its drawback, however, is that the performance of some particular applications could be degraded. On the other hand, the SGluM cache offers more modest improvements in execution time for all the applications by adding some extra structures that cope with the cases in which the lightweight directory fails.     

Visual Navigation for Mobile Robots: A Survey

Mobile robot vision-based navigation has been the source of countless research contributions, from the domains of both vision and control. Vision is becoming more and more common in applications such as localization, automatic map construction, autonomous navigation, path following, inspection, monitoring or risky situation detection. This survey presents those pieces of work, from the nineties until nowadays, which constitute a wide progress in visual navigation techniques for land, aerial and autonomous underwater vehicles. The paper deals with two major approaches: map-based navigation and mapless navigation. Map-based navigation has been in turn subdivided in metric map-based navigation and topological map-based navigation. Our outline to mapless navigation includes reactive techniques based on qualitative characteristics extraction, appearance-based localization, optical flow, features tracking, plane ground detection/tracking, etc... The recent concept of visual sonar has also been revised. This work is partially supported by DPI 2005-09001-C03-02 and FEDER funding.     

A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems

Recommender systems arose with the goal of helping users search in overloaded information domains (like e-commerce, e-learning or Digital TV). These tools automatically select items (commercial products, educational courses, TV programs, etc.) that may be appealing to each user taking into account his/her personal preferences. The personalization strategies used to compare these preferences with the available items suffer from well-known deficiencies that reduce the quality of the recommendations. Most of the limitations arise from using syntactic matching techniques because they miss a lot of useful knowledge during the recommendation process. In this paper, we propose a personalization strategy that overcomes these drawbacks by applying inference techniques borrowed from the Semantic Web. Our approach reasons about the semantics of items and user preferences to discover complex associations between them. These semantic associations provide additional knowledge about the user preferences, and permit the recommender system to compare them with the available items in a more effective way. The proposed strategy is flexible enough to be applied in many recommender systems, regardless of their application domain. Here, we illustrate its use in AVATAR, a tool that selects appealing audiovisual programs from among the myriad available in Digital TV.