Nanomotors: new strategies of (bio)sensing based on motion

Nanomotors are nanoscale devices capable of converting energy into movement and forces. Among them, self-propelled nanomotors offer considerable promise for developing new and novel bioanalytical and biosensing strategies based on the direct isolation of target biomolecules or changes in their movement in the presence of target analytes. The mainachievements of this project consists on the development of receptor-functionalized nanomotors that offer direct and rapid target detection, isolation and transport from raw biological samples without preparatory and washing steps. For example, microtube engines functionalized with aptamer, antibody, lectin and enzymes receptors were used for the direct isolation of analytes of biomedical interest, including proteins and whole cells, among others. A target protein was also isolated from a complex sample by using an antigen-functionalized microengine navigating into the reservoirs of a lab-on-a-chip device. The new nanomotorbased target biomarkers detection strategy not only offers highly sensitive, rapid, simple and low cost alternative for the isolation and transport of target molecules, but also represents a new dimension of analytical information based on motion. The recognition events can be easily visualized by optical microscope (without any sophisticated analytical instrument) to reveal the target presence and concentration. The use of artificial nanomachines has shown not only to be useful for (bio)recognition and (bio)transport but also for detection of environmental contamination and remediation. In this context, micromotors modified with superhydrophobic layer demonstrated that effectively interacted, captured, transported and removed oil droplets from oil contaminated samples. Finally, a unique micromotor-based strategy for water-quality testing, that mimics live-fish water-quality testing, based on changes in the propulsion behavior of artificial biocatalytic microswimmers in the presence of aquatic pollutants was also developed. The attractive features of the new micromachine-based target isolation and signal transduction protocols developed in this project offer numerous potential applications in biomedical diagnostics, environmental monitoring, and forensic analysis.

HSU-Robbins and Spitzer's theorems for the variations of fractional Brownian motion

Using recent results on the behavior of multiple Wiener-Itô integrals based on Stein's method, we prove Hsu-Robbins and Spitzer's theorems for sequences of correlated random variables related to the increments of the fractional Brownian motion.

Environmental Tax on Products and Services Based on Their Carbon Footprint: A Case Study of the Pulp and Paper Sector

The main aim of this work is to define an environmental tax on products and services based on their carbon footprint. We examine the relevance of conventional life cycle analysis (LCA) and environmentally extended input-output analysis (EIO) as methodological tools to identify emission intensities of products and services on which the tax is based. The short-term price effects of the tax and the policy implications of considering non-GHG are also analyzed. The results from the specific case study on pulp production show that the environmental tax rate based on the LCA approach (1,8%) is higher than both EIO approaches (0,8% for product and 1,4% for industry approach), but they are comparable. Even though LCA is more product specific and provides detailed analysis, EIO would be the more relevant approach to apply economy wide environmental tax. When the environmental tax considers non-GHG emissions instead of only CO2, sectors such as agriculture, mining of coal and extraction of peat, and food exhibit higher environmental tax and price effects. Therefore, it is worthwhile for policy makers to pay attention on the implication of considering only CO2 tax or GHG emissions tax in order for such a policy measure to be effective and meaningful. Keywords: Environmental tax; Life cycle analysis; Environmental input-output analysis.

Physically based walking simulation

Hem realitzat l’estudi de moviments humans i hem buscat la forma de poder crear aquests moviments en temps real sobre entorns digitals de forma que la feina que han de dur a terme els artistes i animadors sigui reduïda. Hem fet un estudi de les diferents tècniques d’animació de personatges que podem trobar actualment en l’industria de l’entreteniment així com les principals línies de recerca, estudiant detingudament la tècnica més utilitzada, la captura de moviments. La captura de moviments permet enregistrar els moviments d’una persona mitjançant sensors òptics, sensors magnètics i vídeo càmeres. Aquesta informació és emmagatzemada en arxius que després podran ser reproduïts per un personatge en temps real en una aplicació digital. Tot moviment enregistrat ha d’estar associat a un personatge, aquest és el procés de rigging, un dels punts que hem treballat ha estat la creació d’un sistema d’associació de l’esquelet amb la malla del personatge de forma semi-automàtica, reduint la feina de l’animador per a realitzar aquest procés. En les aplicacions en temps real com la realitat virtual, cada cop més s’està simulant l’entorn en el que viuen els personatges mitjançant les lleis de Newton, de forma que tot canvi en el moviment d’un cos ve donat per l’aplicació d’una força sobre aquest. La captura de moviments no escala bé amb aquests entorns degut a que no és capaç de crear noves animacions realistes a partir de l’enregistrada que depenguin de l’interacció amb l’entorn. L’objectiu final del nostre treball ha estat realitzar la creació d’animacions a partir de forces tal i com ho fem en la realitat en temps real. Per a això hem introduït un model muscular i un sistema de balanç sobre el personatge de forma que aquest pugui respondre a les interaccions amb l’entorn simulat mitjançant les lleis de Newton de manera realista.

An approach to vision-based station keeping for an unmanned underwater vehicle

This paper presents an automatic vision-based system for UUV station keeping. The vehicle is equipped with a down-looking camera, which provides images of the sea-floor. The station keeping system is based on a feature-based motion detection algorithm, which exploits standard correlation and explicit textural analysis to solve the correspondence problem. A visual map of the area surveyed by the vehicle is constructed to increase the flexibility of the system, allowing the vehicle to position itself when it has lost the reference image. The testing platform is the URIS underwater vehicle. Experimental results demonstrating the behavior of the system on a real environment are presented

Perspectives of auto-correcting lens distortions in mosaic-based underwater navigation

When unmanned underwater vehicles (UUVs) perform missions near the ocean floor, optical sensors can be used to improve local navigation. Video mosaics allow to efficiently process the images acquired by the vehicle, and also to obtain position estimates. We discuss in this paper the role of lens distortions in this context, proving that degenerate mosaics have their origin not only in the selected motion model or in registration errors, but also in the cumulative effect of radial distortion residuals. Additionally, we present results on the accuracy of different feature-based approaches for self-correction of lens distortions that may guide the choice of appropriate techniques for correcting distortions

Estimating the motion of an underwater robot from a monocular image sequence

When underwater vehicles perform navigation close to the ocean floor, computer vision techniques can be applied to obtain quite accurate motion estimates. The most crucial step in the vision-based estimation of the vehicle motion consists on detecting matchings between image pairs. Here we propose the extensive use of texture analysis as a tool to ameliorate the correspondence problem in underwater images. Once a robust set of correspondences has been found, the three-dimensional motion of the vehicle can be computed with respect to the bed of the sea. Finally, motion estimates allow the construction of a map that could aid to the navigation of the robot

Towards a real-time vision-based navigation system for a small-class UUV

This paper deals with the problem of navigation for an unmanned underwater vehicle (UUV) through image mosaicking. It represents a first step towards a real-time vision-based navigation system for a small-class low-cost UUV. We propose a navigation system composed by: (i) an image mosaicking module which provides velocity estimates; and (ii) an extended Kalman filter based on the hydrodynamic equation of motion, previously identified for this particular UUV. The obtained system is able to estimate the position and velocity of the robot. Moreover, it is able to deal with visual occlusions that usually appear when the sea bottom does not have enough visual features to solve the correspondence problem in a certain area of the trajectory

Monocular-based 3-D seafloor reconstruction and ortho-mosaicing by piecewise planar representation

Photo-mosaicing techniques have become popular for seafloor mapping in various marine science applications. However, the common methods cannot accurately map regions with high relief and topographical variations. Ortho-mosaicing borrowed from photogrammetry is an alternative technique that enables taking into account the 3-D shape of the terrain. A serious bottleneck is the volume of elevation information that needs to be estimated from the video data, fused, and processed for the generation of a composite ortho-photo that covers a relatively large seafloor area. We present a framework that combines the advantages of dense depth-map and 3-D feature estimation techniques based on visual motion cues. The main goal is to identify and reconstruct certain key terrain feature points that adequately represent the surface with minimal complexity in the form of piecewise planar patches. The proposed implementation utilizes local depth maps for feature selection, while tracking over several views enables 3-D reconstruction by bundle adjustment. Experimental results with synthetic and real data validate the effectiveness of the proposed approach

Predictive motion control of a mirosot mobile robot

This paper discusses predictive motion control of a MiRoSoT robot. The dynamic model of the robot is deduced by taking into account the whole process - robot, vision, control and transmission systems. Based on the obtained dynamic model, an integrated predictive control algorithm is proposed to position precisely with either stationary or moving obstacle avoidance. This objective is achieved automatically by introducing distant constraints into the open-loop optimization of control inputs. Simulation results demonstrate the feasibility of such control strategy for the deduced dynamic model

Comparison of topography- and aperture-dependent motion compensation algorithms for airborne SAR

This letter presents a comparison between threeFourier-based motion compensation (MoCo) algorithms forairborne synthetic aperture radar (SAR) systems. These algorithmscircumvent the limitations of conventional MoCo, namelythe assumption of a reference height and the beam-center approximation.All these approaches rely on the inherent time–frequencyrelation in SAR systems but exploit it differently, with the consequentdifferences in accuracy and computational burden. Aftera brief overview of the three approaches, the performance ofeach algorithm is analyzed with respect to azimuthal topographyaccommodation, angle accommodation, and maximum frequencyof track deviations with which the algorithm can cope. Also, ananalysis on the computational complexity is presented. Quantitativeresults are shown using real data acquired by the ExperimentalSAR system of the German Aerospace Center (DLR).

Economic and environmental effects of the CO2 taxation: an input-output analysis for Spain

CO2 emissions induced by human activities are the major cause of climate change; hence, strong environmental policy that limits the growing dependence on fossil fuel is indispensable. Tradable permits and environmental taxes are the usual tools used in CO2 reduction strategies. Such economic tools provide incentives to polluting industries to reduce their emissions through market signals. The aim of this work is to investigate the direct and indirect effects of an environmental tax on Spanish products and services. We apply an environmentally extended input-output (EIO) model to identify CO2 emission intensities of products and services and, accordingly, we estimate the tax proportional to these intensities. The short-term price effects are analyzed using an input-output price model. The effect of tax introduction on consumption prices and its influence on consumers’ welfare are determined. We also quantify the environmental impacts of such taxation in terms of the reduction in CO2 emissions. The results, based on the Spanish economy for the year 2007, show that sectors with relatively poor environmental profile are subjected to high environmental tax rates. And consequently, applying a CO2 tax on these sectors, increases production prices and induces a slight increase in consumer price index and a decrease in private welfare. The revenue from the tax could be used to counter balance the negative effects on social welfare and also to stimulate the increase of renewable energy shares in the most impacting sectors. Finally, our analysis highlights that the environmental and economic goals cannot be met at the same time with the environmental taxation and this shows the necessity of finding other (complementary or alternative) measures to ensure both the economic and ecological efficiencies. Keywords: CO2 emissions; environmental tax; input-output model, effects of environmental taxation.

Rank estimation of trajectory matrix in motion segmentation

A novel technique for estimating the rank of the trajectory matrix in the local subspace affinity (LSA) motion segmentation framework is presented. This new rank estimation is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built with LSA. The result is an enhanced model selection technique for trajectory matrix rank estimation by which it is possible to automate LSA, without requiring any a priori knowledge, and to improve the final segmentation

Enhanced Model Selection for motion segmentation

In this paper a novel rank estimation technique for trajectories motion segmentation within the Local Subspace Affinity (LSA) framework is presented. This technique, called Enhanced Model Selection (EMS), is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built by LSA. The results on synthetic and real data show that without any a priori knowledge, EMS automatically provides an accurate and robust rank estimation, improving the accuracy of the final motion segmentation

Motion capture amb Microsoft Kinect

El proyecto trata de convertirse en una herramienta para animadores 3D, tanto para los que hacen películas como para los que modelan videojuegos, que necesiten de un software para simplificar el trabajo que conlleva animar un modelo 3D. Todo sin necesidad de usar trajes especializados. El proyecto, usando Kinect, convertirá los movimientos captados por la cámara y los agregará al modelo, creando una animación basándose en los movimientos reales de una persona.