Real time motion estimation using a neural architecture implemented on GPUs

This work describes a neural network based architecture that represents and estimates object motion in videos. This architecture addresses multiple computer vision tasks such as image segmentation, object representation or characterization, motion analysis and tracking. The use of a neural network architecture allows for the simultaneous estimation of global and local motion and the representation of deformable objects. This architecture also avoids the problem of finding corresponding features while tracking moving objects. Due to the parallel nature of neural networks, the architecture has been implemented on GPUs that allows the system to meet a set of requirements such as: time constraints management, robustness, high processing speed and re-configurability. Experiments are presented that demonstrate the validity of our architecture to solve problems of mobile agents tracking and motion analysis.

SARASOM: a supervised architecture based on the recurrent associative SOM

We present and evaluate a novel supervised recurrent neural network architecture, the SARASOM, based on the associative self-organizing map. The performance of the SARASOM is evaluated and compared with the Elman network as well as with a hidden Markov model (HMM) in a number of prediction tasks using sequences of letters, including some experiments with a reduced lexicon of 15 words. The results were very encouraging with the SARASOM learning better and performing with better accuracy than both the Elman network and the HMM.

Association Patterns in Saproxylic Insect Networks in Three Iberian Mediterranean Woodlands and Their Resistance to Microhabitat Loss

The assessment of the relationship between species diversity, species interactions and environmental characteristics is indispensable for understanding network architecture and ecological distribution in complex networks. Saproxylic insect communities inhabiting tree hollow microhabitats within Mediterranean woodlands are highly dependent on woodland configuration and on microhabitat supply they harbor, so can be studied under the network analysis perspective. We assessed the differences in interacting patterns according to woodland site, and analysed the importance of functional species in modelling network architecture. We then evaluated their implications for saproxylic assemblages’ persistence, through simulations of three possible scenarios of loss of tree hollow microhabitat. Tree hollow-saproxylic insect networks per woodland site presented a significant nested pattern. Those woodlands with higher complexity of tree individuals and tree hollow microhabitats also housed higher species/interactions diversity and complexity of saproxylic networks, and exhibited a higher degree of nestedness, suggesting that a higher woodland complexity positively influences saproxylic diversity and interaction complexity, thus determining higher degree of nestedness. Moreover, the number of insects acting as key interconnectors (nodes falling into the core region, using core/periphery tests) was similar among woodland sites, but the species identity varied on each. Such differences in insect core composition among woodland sites suggest the functional role they depict at woodland scale. Tree hollows acting as core corresponded with large tree hollows near the ground and simultaneously housing various breeding microsites, whereas core insects were species mediating relevant ecological interactions within saproxylic communities, e.g. predation, competitive or facilitation interactions. Differences in network patterns and tree hollow characteristics among woodland sites clearly defined different sensitivity to microhabitat loss, and higher saproxylic diversity and woodland complexity showed positive relation with robustness. These results highlight that woodland complexity goes hand in hand with biotic and ecological complexity of saproxylic networks, and together exhibited positive effects on network robustness.

Morphological classification of microtidal sand and gravel beaches

A new classification of microtidal sand and gravel beaches with very different morphologies is presented below. In 557 studied transects, 14 variables were used. Among the variables to be emphasized is the depth of the Posidonia oceanica. The classification was performed for 9 types of beaches: Type 1: Sand and gravel beaches, Type 2: Sand and gravel separated beaches, Type 3: Gravel and sand beaches, Type 4: Gravel and sand separated beaches, Type 5: Pure gravel beaches, Type 6: Open sand beaches, Type 7: Supported sand beaches, Type 8: Bisupported sand beaches and Type 9: Enclosed beaches. For the classification, several tools were used: discriminant analysis, neural networks and Support Vector Machines (SVM), the results were then compared. As there is no theory for deciding which is the most convenient neural network architecture to deal with a particular data set, an experimental study was performed with different numbers of neuron in the hidden layer. Finally, an architecture with 30 neurons was chosen. Different kernels were employed for SVM (Linear, Polynomial, Radial basis function and Sigmoid). The results obtained for the discriminant analysis were not as good as those obtained for the other two methods (ANN and SVM) which showed similar success.

Breaking down complex saproxylic communities: understanding sub-networks structure and implications to network robustness

Saproxylic insect communities inhabiting tree hollow microhabitats correspond with large food webs which simultaneously are constituted by multiple types of plant-animal and animal-animal interactions, according to the use of trophic resources (wood- and insect-dependent sub-networks), or to trophic habits or interaction types (xylophagous, saprophagous, xylomycetophagous, predators and commensals). We quantitatively assessed which properties of specialised networks were present in a complex networks involving different interacting types such as saproxylic community, and how they can be organised in trophic food webs. The architecture, interacting patterns and food web composition were evaluated along sub-networks, analysing their implications to network robustness from random and directed extinction simulations. A structure of large and cohesive modules with weakly connected nodes was observed throughout saproxylic sub-networks, composing the main food webs constituting this community. Insect-dependent sub-networks were more modular than wood-dependent sub-networks. Wood-dependent sub-networks presented higher species degree, connectance, links, linkage density, interaction strength, and were less specialised and more aggregated than insect-dependent sub-networks. These attributes defined high network robustness in wood-dependent sub-networks. Finally, our results emphasise the relevance of modularity, differences among interacting types and interrelations among them in modelling the structure of saproxylic communities and in determining their stability.

A formal framework for modelling complex network management systems

Society today is completely dependent on computer networks, the Internet and distributed systems, which place at our disposal the necessary services to perform our daily tasks. Subconsciously, we rely increasingly on network management systems. These systems allow us to, in general, maintain, manage, configure, scale, adapt, modify, edit, protect, and enhance the main distributed systems. Their role is secondary and is unknown and transparent to the users. They provide the necessary support to maintain the distributed systems whose services we use every day. If we do not consider network management systems during the development stage of distributed systems, then there could be serious consequences or even total failures in the development of the distributed system. It is necessary, therefore, to consider the management of the systems within the design of the distributed systems and to systematise their design to minimise the impact of network management in distributed systems projects. In this paper, we present a framework that allows the design of network management systems systematically. To accomplish this goal, formal modelling tools are used for modelling different views sequentially proposed of the same problem. These views cover all the aspects that are involved in the system; based on process definitions for identifying responsible and defining the involved agents to propose the deployment in a distributed architecture that is both feasible and appropriate.

A Computational Architecture Based on RFID Sensors for Traceability in Smart Cities

Information Technology and Communications (ICT) is presented as the main element in order to achieve more efficient and sustainable city resource management, while making sure that the needs of the citizens to improve their quality of life are satisfied. A key element will be the creation of new systems that allow the acquisition of context information, automatically and transparently, in order to provide it to decision support systems. In this paper, we present a novel distributed system for obtaining, representing and providing the flow and movement of people in densely populated geographical areas. In order to accomplish these tasks, we propose the design of a smart sensor network based on RFID communication technologies, reliability patterns and integration techniques. Contrary to other proposals, this system represents a comprehensive solution that permits the acquisition of user information in a transparent and reliable way in a non-controlled and heterogeneous environment. This knowledge will be useful in moving towards the design of smart cities in which decision support on transport strategies, business evaluation or initiatives in the tourism sector will be supported by real relevant information. As a final result, a case study will be presented which will allow the validation of the proposal.

Conceptual Modelling of Complex Network Management Systems

Society, as we know it today, is completely dependent on computer networks, Internet and distributed systems, which place at our disposal the necessary services to perform our daily tasks. Moreover, and unconsciously, all services and distributed systems require network management systems. These systems allow us to, in general, maintain, manage, configure, scale, adapt, modify, edit, protect or improve the main distributed systems. Their role is secondary and is unknown and transparent to the users. They provide the necessary support to maintain the distributed systems whose services we use every day. If we don’t consider network management systems during the development stage of main distributed systems, then there could be serious consequences or even total failures in the development of the distributed systems. It is necessary, therefore, to consider the management of the systems within the design of distributed systems and systematize their conception to minimize the impact of the management of networks within the project of distributed systems. In this paper, we present a formalization method of the conceptual modelling for design of a network management system through the use of formal modelling tools, thus allowing from the definition of processes to identify those responsible for these. Finally we will propose a use case to design a conceptual model intrusion detection system in network.

An Electronic Reconfigurable Neural Architecture for Intrusion Detection

The explosive growth of the traffic in computer systems has made it clear that traditional control techniques are not adequate to provide the system users fast access to network resources and prevent unfair uses. In this paper, we present a reconfigurable digital hardware implementation of a specific neural model for intrusion detection. It uses a specific vector of characterization of the network packages (intrusion vector) which is starting from information obtained during the access intent. This vector will be treated by the system. Our approach is adaptative and to detecting these intrusions by using a complex artificial intelligence method known as multilayer perceptron. The implementation have been developed and tested into a reconfigurable hardware (FPGA) for embedded systems. Finally, the Intrusion detection system was tested in a real-world simulation to gauge its effectiveness and real-time response.