Quality coding by neural populations in the early olfactory pathway: analysis using information theory

In this article, we analyze the ability of the early olfactory system to detect and discriminate different odors by means of information theory measurements applied to olfactory bulb activity images. We have studied the role that the diversity and number of receptor neuron types play in encoding chemical information. Our results show that the olfactory receptors of the biological system are low correlated and present good coverage of the input space. The coding capacity of ensembles of olfactory receptors with the same receptive range is maximized when the receptors cover half of the odor input space - a configuration that corresponds to receptors that are not particularly selective. However, the ensemble's performance slightly increases when mixing uncorrelated receptors of different receptive ranges. Our results confirm that the low correlation between sensors could be more significant than the sensor selectivity for general purpose chemo-sensory systems, whether these are biological or biomimetic.

Hierarchical neural network with high storage capacity

A recent method used to optimize biased neural networks with low levels of activity is applied to a hierarchical model. As a consequence, the performance of the system is strongly enhanced. The steps to achieve optimization are analyzed in detail.

Noise and periodic modulations in neural excitable medium

We have analyzed the interplay between noise and periodic modulations in a mean field model of a neural excitable medium. For this purpose, we have considered two types of modulations, namely, variations of the resistance and oscillations of the threshold. In both cases, stochastic resonance is present, irrespective of whether the system is monostable or bistable.

Podocalyxin is a novel polysialylated neural adhesion protein with multiple roles in neural development and synapse formation

Neural development and plasticity are regulated by neural adhesion proteins, including the polysialylated form of NCAM (PSA-NCAM). Podocalyxin (PC) is a renal PSA-containing protein that has been reported to function as an anti-adhesin in kidney podocytes. Here we show that PC is widely expressed in neurons during neural development. Neural PC interacts with the ERM protein family, and with NHERF1/2 and RhoA/G. Experiments in vitro and phenotypic analyses of podxl-deficient mice indicate that PC is involved in neurite growth, branching and axonal fasciculation, and that PC loss-of-function reduces the number of synapses in the CNS and in the neuromuscular system. We also show that whereas some of the brain PC functions require PSA, others depend on PC per se. Our results show that PC, the second highly sialylated neural adhesion protein, plays multiple roles in neural development.

Reconfigurable Architecture To Estimate The Motion Of An Underwater Vehicle

This work proposes a parallel architecture for a motion estimation algorithm. It is well known that image processing requires a huge amount of computation, mainly at low level processing where the algorithms are dealing with a great numbers of data-pixel. One of the solutions to estimate motions involves detection of the correspondences between two images. Due to its regular processing scheme, parallel implementation of correspondence problem can be an adequate approach to reduce the computation time. This work introduces parallel and real-time implementation of such low-level tasks to be carried out from the moment that the current image is acquired by the camera until the pairs of point-matchings are detected

Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants

This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen

SiMR: A simulator for learning computer architecture

This paper presents SiMR, a simulator of the Rudimentary Machine designed to be used in a first course of computer architecture of Software Engineering and Computer Engineering programmes. The Rudimentary Machine contains all the basic elements in a RISC computer, and SiMR allows editing, assembling and executing programmes for this processor. SiMR is used at the Universitat Oberta de Catalunya as one of the most important resources in the Virtual Computing Architecture and Organisation Laboratory, since students work at home with the simulator and reports containing their work are automatically generated to be evaluated by lecturers. The results obtained from a survey show that most of the students consider SiMR as a highly necessary or even an indispensable resource to learn the basic concepts about computer architecture.

Final architecture diploma projects in the analysis of the UPC buildings energy performance

A partir de maig de 2003, per iniciativa del Vicerectorat adjunt d’Edificacions de la UPC, el Centre Interdisciplinari de Tecnologia, Innovació i Educació per a la Sostenibilitat (CITIES) treballa en l’elaboració i la implantació del Pla d’Eficiència en el Consum de Recursos (PECR), amb l’objectiu d’establir polítiques i definir línees d’actuació per a l’estalvi i l’eficiència en el consum dels recursos energètics i d’ aigua en els edificis de la UPC.El PECR contempla, en una de les primeres fases, la realització d’avaluacions energètiques en les edificacions de la UPC per valorar l’estat actual dels edificis i poder establir uns indicadors del seu comportament energètic a partir dels quals establir els objectius d’estalvi i d’eficiència. Per fer aquestes avaluacions, es va crear una línea de projectes finals de carrera (PFC) per a estudiants de l’Escola Politècnica Superior de l’Edificació de Barcelona (EPSEB), sota la coordinació de professors tutors de diferents departaments y amb la col•laboració indispensable de totes les unitats de recolzament de la UPC.Aquesta publicació és la ponència presentada al IV Congrès "Sustainable City", a Tallinn, en el que es va presentar aquest projecte com a una eina de treball amb l'objectiu de reduir l'impacte ambiental dels edificis universitaris en les ciutats.

A Middleware architecture for Unmanned Aircraft Avionics

An Unmanned Aerial Vehicle is a non-piloted airplane designed to operate in dangerous and repetitive situations. With the advent of UAV's civil applications, UAVs are emerging as a valid option in commercial scenarios. If it must be economically viable, the same platform should implement avariety of missions with little reconguration time and overhead.This paper presents a middleware-based architecture specially suited to operate as a exible payload and mission controller in a UAV. The system is composed of low-costcomputing devices connected by network. The functionality is divided into reusable services distributed over a number ofnodes with a middleware managing their lifecycle and communication.Some research has been done in this area; yetit is mainly focused on the control domain and in its realtime operation. Our proposal differs in that we address the implementation of adaptable and reconfigurable unmannedmissions in low-cost and low-resources hardware.

Forecasting tourism demand to Catalonia: neural networks vs. time series models

The increasing interest aroused by more advanced forecasting techniques, together with the requirement for more accurate forecasts of tourismdemand at the destination level due to the constant growth of world tourism, has lead us to evaluate the forecasting performance of neural modelling relative to that of time seriesmethods at a regional level. Seasonality and volatility are important features of tourism data, which makes it a particularly favourable context in which to compare the forecasting performance of linear models to that of nonlinear alternative approaches. Pre-processed official statistical data of overnight stays and tourist arrivals fromall the different countries of origin to Catalonia from 2001 to 2009 is used in the study. When comparing the forecasting accuracy of the different techniques for different time horizons, autoregressive integrated moving average models outperform self-exciting threshold autoregressions and artificial neural network models, especially for shorter horizons. These results suggest that the there is a trade-off between the degree of pre-processing and the accuracy of the forecasts obtained with neural networks, which are more suitable in the presence of nonlinearity in the data. In spite of the significant differences between countries, which can be explained by different patterns of consumer behaviour,we also find that forecasts of tourist arrivals aremore accurate than forecasts of overnight stays.

Domestic architecture and social differences in North-Eastern Iberia during the iron age (c.525-200 BC)

This paper is concerned with the organization of societies in north-eastern Iberia (present-day Catalonia) during the Iron Age, using data provided by domestic architecture and settlement organization. I offer an analysis of the social differences detected in the dwellings based on a sample of houses excavated at different types of settlement. Although many Iberian houses had simple layouts and small surface areas, some larger dwellings at the main sites are distinguished by the shape of their ground plans, their surface areas, architectural features, and central locations; these houses are believed to be the residences of the Iberian elite. Such dwellings are not found at all sites and the data suggest that there was a relationship between the category of the settlement (or its function) and the types of dwelling in it.

Information architecture for the semantic web

L'objectiu és desenvolupar i avaluar una sèrie de components de l'arquitectura de la informació per a la web semàntica. Aquest components són genèrics i permeten als usuaris explorar conjunts de dades semàntiques sense necessitat de conèixer l'estructura ni tenir coneixements tècnics. S'ha desenvolupat seguint una metodologia de disseny centrat en l'usuari

A design for an advanced architecture of satellite ground segments

This project proposes a preliminary architectural design for a control and data processing center, also known as 'ground segment', for Earth observation satellites.

Transfer entropy reconstruction and labeling of neuronal connections from simulated calcium imaging

Neuronal dynamics are fundamentally constrained by the underlying structural network architecture, yet much of the details of this synaptic connectivity are still unknown even in neuronal cultures in vitro. Here we extend a previous approach based on information theory, the Generalized Transfer Entropy, to the reconstruction of connectivity of simulated neuronal networks of both excitatory and inhibitory neurons. We show that, due to the model-free nature of the developed measure, both kinds of connections can be reliably inferred if the average firing rate between synchronous burst events exceeds a small minimum frequency. Furthermore, we suggest, based on systematic simulations, that even lower spontaneous inter-burst rates could be raised to meet the requirements of our reconstruction algorithm by applying a weak spatially homogeneous stimulation to the entire network. By combining multiple recordings of the same in silico network before and after pharmacologically blocking inhibitory synaptic transmission, we show then how it becomes possible to infer with high confidence the excitatory or inhibitory nature of each individual neuron.

Integrating defeasible argumentation with fuzzy ART neural networks for pattern classification

Many classification systems rely on clustering techniques in which a collection of training examples is provided as an input, and a number of clusters c1,...cm modelling some concept C results as an output, such that every cluster ci is labelled as positive or negative. Given a new, unlabelled instance enew, the above classification is used to determine to which particular cluster ci this new instance belongs. In such a setting clusters can overlap, and a new unlabelled instance can be assigned to more than one cluster with conflicting labels. In the literature, such a case is usually solved non-deterministically by making a random choice. This paper presents a novel, hybrid approach to solve this situation by combining a neural network for classification along with a defeasible argumentation framework which models preference criteria for performing clustering.