Catálogo de publicaciones - revistas

<jats:p>Networks-on-Chip constitute the interconnection architecture of future, massively parallel multiprocessors that assemble hundreds to thousands of processing cores on a single chip. Their integration is enabled by ongoing miniaturization of chip manufacturing technologies following Moore's Law. It comes with the downside of the circuit elements' increased susceptibility to failure. Research on fault-tolerant Networks-on-Chip tries to mitigate partial failure and its effect on network performance and reliability by exploiting various forms of redundancy at the suitable network layers. The article at hand reviews the failure mechanisms, fault models, diagnosis techniques, and fault-tolerance methods in on-chip networks, and surveys and summarizes the research of the last ten years. It is structured along three communication layers: the data link, the network, and the transport layers. The most important results are summarized and open research problems and challenges are highlighted to guide future research on this topic.</jats:p>

<jats:p>A number of research studies considering a self-organizing map have been developed since such a map was proposed by Kohonen [1982]. Some of these studies concern SOM-based models that do not use pre-defined structures to produce their mappings. We call these models Self-Organizing Maps with Time-Varying Structure (SOM-TVS). Despite the large number of SOM-TVS models there is not a standard way to describe them. In this article, we propose a framework to describe SOM-TVS models, which we use to describe some of these models and to compare their algorithms, and we present some real-world applications of the models presented.</jats:p>

<jats:p>The development of wireless sensor networks, such as researchers Advanced Driver Assistance Systems (ADAS) requires the ability to analyze the road scene just like a human does. Road scene analysis is an essential, complex, and challenging task and it consists of: road detection (which includes the localization of the road, the determination of the relative position between vehicle and road, and the analysis of the vehicle's heading direction) and obstacle detection (which is mainly based on localizing possible obstacles on the vehicle's path). The detection of the road borders, the estimation of the road geometry, and the localization of the vehicle are essential tasks in this context since they are required for the lateral and longitudinal control of the vehicle. Within this field, on-board vision has been widely used since it has many advantages (higher resolution, low power consumption, low cost, easy aesthetic integration, and nonintrusive nature) over other active sensors such as RADAR or LIDAR. At first glance the problem of detecting the road geometry from visual information seems simple and early works in this field were quickly rewarded with promising results. However, the large variety of scenarios and the high rates of success demanded by the industry have kept the lane detection research work alive. In this article a comprehensive review of vision-based road detection systems vision is presented.</jats:p>

<jats:p> In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large-scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling, and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a <jats:italic>family</jats:italic> of approaches and mechanisms of large-scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large-scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions. </jats:p>

<jats:p>Scalability, fast response time, and low cost are of utmost importance in designing a successful massively multiplayer online game. The underlying architecture plays an important role in meeting these conditions. Peer-to-peer architectures, due to their distributed and collaborative nature, have low infrastructure costs and can achieve high scalability. They can also achieve fast response times by creating direct connections between players. However, these architectures face many challenges. Distributing a game among peers makes maintaining control over the game more complex. Peer-to-peer architectures also tend to be vulnerable to churn and cheating. Moreover, different genres of games have different requirements that should be met by the underlying architecture, rendering the task of designing a general-purpose architecture harder. Many peer-to-peer gaming solutions have been proposed that utilize a range of techniques while using somewhat different and confusing terminologies. This article presents a comprehensive overview of current peer-to-peer solutions for massively multiplayer games using a uniform terminology.</jats:p>

<jats:p>Object class detection, also known as category-level object detection, has become one of the most focused areas in computer vision in the new century. This article attempts to provide a comprehensive survey of the recent technical achievements in this area of research. More than 270 major publications are included in this survey covering different aspects of the research, which include: (i) problem description: key tasks and challenges; (ii) core techniques: appearance modeling, localization strategies, and supervised classification methods; (iii) evaluation issues: approaches, metrics, standard datasets, and state-of-the-art results; and (iv) new development: particularly new approaches and applications motivated by the recent boom of social images. Finally, in retrospect of what has been achieved so far, the survey also discusses what the future may hold for object class detection research.</jats:p>

<jats:p>Quality of service (QoS) can be a critical element for achieving the business goals of a service provider, for the acceptance of a service by the user, or for guaranteeing service characteristics in a composition of services, where a service is defined as either a software or a software-support (i.e., infrastructural) service which is available on any type of network or electronic channel. The goal of this article is to compare the approaches to QoS description in the literature, where several models and metamodels are included. consider a large spectrum of models and metamodels to describe service quality, ranging from ontological approaches to define quality measures, metrics, and dimensions, to metamodels enabling the specification of quality-based service requirements and capabilities as well as of SLAs (Service-Level Agreements) and SLA templates for service provisioning. Our survey is performed by inspecting the characteristics of the available approaches to reveal which are the consolidated ones and which are the ones specific to given aspects and to analyze where the need for further research and investigation lies. The approaches here illustrated have been selected based on a systematic review of conference proceedings and journals spanning various research areas in computer science and engineering, including: distributed, information, and telecommunication systems, networks and security, and service-oriented and grid computing.</jats:p>

<jats:p>Steganography is an ancient art that encompasses various techniques of information hiding, the aim of which is to embed secret information into a carrier message. Steganographic methods are usually aimed at hiding the very existence of the communication. Due to the rise in popularity of IP telephony, together with the large volume of data and variety of protocols involved, it is currently attracting the attention of the research community as a perfect carrier for steganographic purposes. This article is a first survey of the existing Voice over IP (VoIP) steganography methods and their countermeasures.</jats:p>

<jats:p>In recent years, a large number of research projects have focused on the use of auditory representations in a broadened scope of application scenarios. Results in such projects have shown that auditory elements can effectively complement other modalities not only in the traditional desktop computer environment but also in virtual and augmented reality, mobile platforms, and other kinds of novel computing environments. The successful use of auditory representations in this growing number of application scenarios has in turn prompted researchers to rediscover the more basic auditory representations and extend them in various directions. The goal of this article is to survey both classical auditory representations (e.g., auditory icons and earcons) and those auditory representations that have been created as extensions to earlier approaches, including speech-based sounds (e.g., spearcons and spindex representations), emotionally grounded sounds (e.g., auditory emoticons and spemoticons), and various other sound types used to provide sonifications in practical scenarios. The article concludes by outlining the latest trends in auditory interface design and providing examples of these trends.</jats:p>

<jats:p>Literature in psychology has shown that curiosity is the intrinsic motivation for exploration, learning, and creativity. Various forms of computational curiosity have been developed to provide artificial beings with desirable functions, such as detecting and adapting to novel inputs, making decisions related to aesthetics, and achieving pedagogical purposes. This article reviews existing models of computational curiosity in light of psychological theories that are beneficial to building models of human cognition and designing human-like agents. We first study theories in psychology to shed light on the underpinnings of human curiosity, where a two-step process is proposed to serve as a general model for analyzing curiosity. Subsequently, existing models of computational curiosity are reviewed under the proposed framework. We conclude the review by identifying 4 key research issues in computational curiosity and 10 important research areas where computational curiosity could bring significant impact.</jats:p>