Catálogo de publicaciones - revistas

<jats:p>Despite surveillance systems becoming increasingly ubiquitous in our living environment, automated surveillance, currently based on video sensory modality and machine intelligence, lacks most of the time the robustness and reliability required in several real applications. To tackle this issue, audio sensory devices have been incorporated, both alone or in combination with video, giving birth in the past decade, to a considerable amount of research. In this article, audio-based automated surveillance methods are organized into a comprehensive survey: A general taxonomy, inspired by the more widespread video surveillance field, is proposed to systematically describe the methods covering background subtraction, event classification, object tracking, and situation analysis. For each of these tasks, all the significant works are reviewed, detailing their pros and cons and the context for which they have been proposed. Moreover, a specific section is devoted to audio features, discussing their expressiveness and their employment in the above-described tasks. Differing from other surveys on audio processing and analysis, the present one is specifically targeted to automated surveillance, highlighting the target applications of each described method and providing the reader with a systematic and schematic view useful for retrieving the most suited algorithms for each specific requirement.</jats:p>

<jats:p>While Wireless Sensor Networks (WSNs) have been traditionally tasked with single applications, in recent years we have witnessed the emergence of Shared Sensor Networks (SSNs) as integrated cyber-physical system infrastructures for a multitude of applications. Instead of assuming an application-specific network design, SSNs allow the underlying infrastructure to be shared among multiple applications that can potentially belong to different users. On one hand, a potential benefit of such a design approach is to increase the utilization of sensing and communication resources, whenever the underlying network infrastructure covers the same geographic area and the sensor nodes monitor the same physical variables of common interest for different applications. On the other hand, compared with the existing application-specific design, the SSNs approach poses several research challenges with regard to different aspects of WSNs. In this article, we present a systematic literature survey on SSNs. The main goal of the article is to provide the reader with the opportunity to understand what has been done and what remains as open issues in this field, as well as which are the pivotal factors of this evolutionary design and how this kind of design can be exploited by a wide range of WSN applications.</jats:p>

<jats:p>Understanding human behavior in an automatic but nonintrusive manner is an important area for various applications. This requires the collaboration of information technology with human sciences to transfer existing knowledge of human behavior into self-acting tools. These tools will reduce human error that is introduced by current obtrusive methods such as questionnaires. To achieve unobtrusiveness, we focus on exploiting the pervasive and ubiquitous character of mobile devices.</jats:p> <jats:p> In this article, a survey of existing techniques for extracting social behavior through mobile devices is provided. Initially, we expose the terminology used in the area and introduce a concrete architecture for social signal processing applications on mobile phones, constituted by <jats:italic>sensing</jats:italic> , <jats:italic>social interaction detection</jats:italic> , <jats:italic>behavioral cues extraction</jats:italic> , <jats:italic>social signal inference,</jats:italic> and <jats:italic>social behavior understanding</jats:italic> . Furthermore, we present state-of-the-art techniques applied to each stage of the process. Finally, potential applications are shown while arguing about the main challenges of the area. </jats:p>

<jats:p>Sensors have limited precision and accuracy. They extract data from the physical environment, which contains noise. The goal of sensor fusion is to make the final decision robust, minimizing the influence of noise and system errors. One problem that has not been adequately addressed is establishing the bounds of fusion result precision. Precision is the maximum range of disagreement that can be introduced by one or more faulty inputs. This definition of precision is consistent both with Lamport’s Byzantine Generals problem and the mini-max criteria commonly found in game theory. This article considers the precision bounds of several fault-tolerant information fusion approaches, including Byzantine agreement, Marzullo’s interval-based approach, and the Brooks-Iyengar fusion algorithm. We derive precision bounds for these fusion algorithms. The analysis provides insight into the limits imposed by fault tolerance and guidance for applying fusion approaches to applications.</jats:p>

<jats:p>One of the most widely used biomimicry algorithms is the Particle Swarm Optimization (PSO). Since its introduction in 1995, it has caught the attention of both researchers and academicians as a way of solving various optimization problems, such as in the fields of engineering and medicine, to computer image processing and mission critical operations. PSO has been widely applied in the field of swarm robotics, however, the trend of creating a new variant PSO for each swarm robotic project is alarming. We investigate the basic properties of PSO algorithms relevant to the implementation of swarm robotics and characterize the limitations that promote this trend to manifest. Experiments were conducted to investigate the convergence properties of three PSO variants (original PSO, SPSO and APSO) and the global optimum and local optimal of these PSO algorithms were determined. We were able to validate the existence of premature convergence in these PSO variants by comparing 16 functions implemented alongside the PSO variant. This highlighted the fundamental flaws in most variant PSOs, and signifies the importance of developing a more generalized PSO algorithm to support the implementation of swarm robotics. This is critical in curbing the influx of custom PSO and theoretically addresses the fundamental flaws of the existing PSO algorithm.</jats:p>

<jats:p>Twitter is a microblogging platform in which users can post status messages, called “tweets,” to their friends. It has provided an enormous dataset of the so-called sentiments, whose classification can take place through supervised learning. To build supervised learning models, classification algorithms require a set of representative labeled data. However, labeled data are usually difficult and expensive to obtain, which motivates the interest in semi-supervised learning. This type of learning uses unlabeled data to complement the information provided by the labeled data in the training process; therefore, it is particularly useful in applications including tweet sentiment analysis, where a huge quantity of unlabeled data is accessible. Semi-supervised learning for tweet sentiment analysis, although appealing, is relatively new. We provide a comprehensive survey of semi-supervised approaches applied to tweet classification. Such approaches consist of graph-based, wrapper-based, and topic-based methods. A comparative study of algorithms based on self-training, co-training, topic modeling, and distant supervision highlights their biases and sheds light on aspects that the practitioner should consider in real-world applications.</jats:p>

<jats:p>Peer-to-peer technologies have proven their strength in large-scale resource sharing and data transfer. Such systems, however, still need to address a variety of issues, including efficient routing, security, quality of service, incentives, and reputation. Recent research started leveraging social information to develop new and effective techniques to improve the performance of peer-to-peer systems. However, using social information is a double-edged sword, which can bring benefits as well as new challenges. This survey presents and classifies the types of social information that have been used so far in the design of peer-to-peer systems, how the social fabric has been used to facilitate transactions in the system, and some challenges caused by using social information.</jats:p>

<jats:p>In recent years, distributed intelligent microelectromechanical systems (DiMEMSs) have appeared as a new form of distributed embedded systems. DiMEMSs contain thousands or millions of removable autonomous devices, which will collaborate with each other to achieve the final target of the whole system. Programming such systems is becoming an extremely difficult problem. The difficulty is due not only to their inherent nature of distributed collaboration, mobility, large scale, and limited resources of their devices (e.g., in terms of energy, memory, communication, and computation) but also to the requirements of real-time control and tolerance for uncertainties such as inaccurate actuation and unreliable communications. As a result, existing programming languages for traditional distributed and embedded systems are not suitable for DiMEMSs. In this article, we first introduce the origin and characteristics of DiMEMSs and then survey typical implementations of DiMEMSs and related research hotspots. Finally, we propose a real-time programming framework that can be used to design new real-time programming languages for DiMEMSs. The framework is composed of three layers: a real-time programming model layer, a compilation layer, and a runtime system layer. The design challenges and requirements of these layers are investigated. The framework is then discussed in further detail and suggestions for future research are given.</jats:p>

<jats:p>A significant number of promising applications for vehicular ad hoc networks (VANETs) are becoming a reality. Most of these applications require a variety of heterogenous content to be delivered to vehicles and to their on-board users. However, the task of content delivery in such dynamic and large-scale networks is easier said than done. In this article, we propose a classification of content delivery solutions applied to VANETs while highlighting their new characteristics and describing their underlying architectural design. First, the two fundamental building blocks that are part of an entire content delivery system are identified: replica allocation and content delivery. The related solutions are then classified according to their architectural definition. Within each category, solutions are described based on the techniques and strategies that have been adopted. As result, we present an in-depth discussion on the architecture, techniques, and strategies adopted by studies in the literature that tackle problems related to vehicular content delivery networks.</jats:p>

<jats:p>A smart power grid transforms the traditional electric grid into a user-centric, intelligent power network. The cost-saving potential of smart homes is an excellent motivating factor to involve users in smart grid operations. To that end, this survey explores the contemporary cost-saving strategies for smart grids from the users’ perspective. The study shows that optimization methods are the most popular cost-saving techniques reported in the literature. These methods are used to plan scheduling and power utilization schemes of household appliances, energy storages, renewables, and other energy generation devices. The survey shows that trading energy among neighborhoods is one of the effective methods for cost optimization. It also identifies the prediction methods that are used to forecast energy price, generation, and consumption profiles, which are required to optimize energy cost in advance. The contributions of this article are threefold. First, it discusses the computational methods reported in the literature with their significance and limitations. Second, it identifies the components and their characteristics that may reduce energy cost. Finally, it proposes a unified cost optimization framework and addresses the challenges that may influence the overall residential energy cost optimization problem in smart grids.</jats:p>