Towards a real-time 3D object recognition pipeline on mobile GPGPU computing platforms using low-cost RGB-D sensors

In this project, we propose the implementation of a 3D object recognition system which will be optimized to operate under demanding time constraints. The system must be robust so that objects can be recognized properly in poor light conditions and cluttered scenes with significant levels of occlusion. An important requirement must be met: the system must exhibit a reasonable performance running on a low power consumption mobile GPU computing platform (NVIDIA Jetson TK1) so that it can be integrated in mobile robotics systems, ambient intelligence or ambient assisted living applications. The acquisition system is based on the use of color and depth (RGB-D) data streams provided by low-cost 3D sensors like Microsoft Kinect or PrimeSense Carmine. The range of algorithms and applications to be implemented and integrated will be quite broad, ranging from the acquisition, outlier removal or filtering of the input data and the segmentation or characterization of regions of interest in the scene to the very object recognition and pose estimation. Furthermore, in order to validate the proposed system, we will create a 3D object dataset. It will be composed by a set of 3D models, reconstructed from common household objects, as well as a handful of test scenes in which those objects appear. The scenes will be characterized by different levels of occlusion, diverse distances from the elements to the sensor and variations on the pose of the target objects. The creation of this dataset implies the additional development of 3D data acquisition and 3D object reconstruction applications. The resulting system has many possible applications, ranging from mobile robot navigation and semantic scene labeling to human-computer interaction (HCI) systems based on visual information.

Flexible processing architecture for maintaining QoS in embedded systems applications

Comunicación presentada en las V Jornadas de Computación Empotrada, Valladolid, 17-19 Septiembre 2014

Direct electrospinning of 3D auricle-shaped scaffolds for tissue engineering applications.

Thirty-two poly(ε)caprolactone (PCL) scaffolds have been produced by electrospinning directly into an auricle-shaped mould and seeded with articular chondrocytes harvested from bovine ankle joints. After seeding, the auricle shaped constructs were cultured in vitro and analysed at days 1, 7, 14 and 21 for regional differences in total DNA, glycosaminoglycan (GAG) and collagen (COL) content as well as the expression of aggrecan (AGG), collagen type I and type II (COL1/2) and matrix metalloproteinase 3 and 13 (MMP3/13). Stress-relaxation indentation testing was performed to investigate regional mechanical properties of the electrospun constructs. Electrospinning into a conductive mould yielded stable 3D constructs both initially and for the whole in vitro culture period, with an equilibrium modulus in the MPa range. Rapid cell proliferation and COL accumulation was observed until week 3. Quantitative real time PCR analysis showed an initial increase in AGG, no change in COL2, a persistent increase in COL1, and only a slight decrease initially for MMP3. Electrospinning of fibrous scaffolds directly into an auricle-shape represents a promising option for auricular tissue engineering, as it can reduce the steps needed to achieve an implantable structure.

Dynamic D-Vine copula model with applications to Value-at-Risk (VaR)

Regular vine copulas are multivariate dependence models constructed from pair-copulas (bivariate copulas). In this paper, we allow the dependence parameters of the pair-copulas in a D-vine decomposition to be potentially time-varying, following a nonlinear restricted ARMA(1,m) process, in order to obtain a very flexible dependence model for applications to multivariate financial return data. We investigate the dependence among the broad stock market indexes from Germany (DAX), France (CAC 40), Britain (FTSE 100), the United States (S&P 500) and Brazil (IBOVESPA) both in a crisis and in a non-crisis period. We find evidence of stronger dependence among the indexes in bear markets. Surprisingly, though, the dynamic D-vine copula indicates the occurrence of a sharp decrease in dependence between the indexes FTSE and CAC in the beginning of 2011, and also between CAC and DAX during mid-2011 and in the beginning of 2008, suggesting the absence of contagion in these cases. We also evaluate the dynamic D-vine copula with respect to Value-at-Risk (VaR) forecasting accuracy in crisis periods. The dynamic D-vine outperforms the static D-vine in terms of predictive accuracy for our real data sets.

Introduction a l'électricité : contenant les notions exactes du feu élémentaire, avec leurs applications à nombre de phénomenes de physique, de chymie & d'économie animale.

Mode of access: Internet.

A study of the problems of the distressed home owner of Detroit as revealed by applications to the Home Owners' Loan Corporation; report to the Earhart Foundation.

Mode of access: Internet.

Dynamic multi-resolution spatial object derivation for mobile and WWW applications

Online geographic information systems provide the means to extract a subset of desired spatial information from a larger remote repository. Data retrieved representing real-world geographic phenomena are then manipulated to suit the specific needs of an end-user. Often this extraction requires the derivation of representations of objects specific to a particular resolution or scale from a single original stored version. Currently standard spatial data handling techniques cannot support the multi-resolution representation of such features in a database. In this paper a methodology to store and retrieve versions of spatial objects at, different resolutions with respect to scale using standard database primitives and SQL is presented. The technique involves heavy fragmentation of spatial features that allows dynamic simplification into scale-specific object representations customised to the display resolution of the end-user's device. Experimental results comparing the new approach to traditional R-Tree indexing and external object simplification reveal the former performs notably better for mobile and WWW applications where client-side resources are limited and retrieved data loads are kept relatively small.

Process systems modelling and applications in granulation: A review

Granulation is one of the fundamental operations in particulate processing and has a very ancient history and widespread use. Much fundamental particle science has occurred in the last two decades to help understand the underlying phenomena. Yet, until recently the development of granulation systems was mostly based on popular practice. The use of process systems approaches to the integrated understanding of these operations is providing improved insight into the complex nature of the processes. Improved mathematical representations, new solution techniques and the application of the models to industrial processes are yielding better designs, improved optimisation and tighter control of these systems. The parallel development of advanced instrumentation and the use of inferential approaches provide real-time access to system parameters necessary for improvements in operation. The use of advanced models to help develop real-time plant diagnostic systems provides further evidence of the utility of process system approaches to granulation processes. This paper highlights some of those aspects of granulation. (c) 2005 Elsevier Ltd. All rights reserved.

Real-time reverse transcriptase PCR for the endogenous koala retrovirus reveals an association between plasma viral load and neoplastic disease in koalas

Koala retrovirus (KoRV) is a newly described endogenous retrovirus and is unusual in that inserts comprise a full-length replication competent genome. As koalas are known to suffer from an extremely high incidence of leukaemia/lymphoma, the association between this retrovirus and disease in koalas was examined. Using quantitative real-time reverse transcriptase PCR it was demonstrated that KoRV RNA levels in plasma are significantly increased in animals suffering from leukaemia or lymphoma when compared with healthy animals. Increased levels of KoRV were also seen for animals with clinical chlamydiosis. A significant positive association between viral RNA levels and age was also demonstrated. Real-time PCR demonstrated as much as 5 log variation in KoRV proviral DNA levels in genomic DNA extracted from whole blood from different animals. Taken together these data indicate that KoRV is an active endogenous retrovirus and suggests that it may be causally linked to neoplastic disease in koalas.

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease. Results We show that GPNN has high power to detect even relatively small genetic effects (2–3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (

Tyre-force estimation by Kalman inverse filtering: applications to off-highway mining trucks

Kalman inverse filtering is used to develop a methodology for real-time estimation of forces acting at the interface between tyre and road on large off-highway mining trucks. The system model formulated is capable of estimating the three components of tyre-force at each wheel of the truck using a practical set of measurements and inputs. Good tracking is obtained by the estimated tyre-forces when compared with those simulated by an ADAMS virtual-truck model. A sensitivity analysis determines the susceptibility of the tyre-force estimates to uncertainties in the truck's parameters.

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background: The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease. Results: We show that GPNN has high power to detect even relatively small genetic effects (2-3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (

Towards platform-independent real-time systems

Real-time software systems are rarely developed once and left to run. They are subject to changes of requirements as the applications they support expand, and they commonly outlive the platforms they were designed to run on. A successful real-time system is duplicated and adapted to a variety of applications - it becomes a product line. Current methods for real-time software development are commonly based on low-level programming languages and involve considerable duplication of effort when a similar system is to be developed or the hardware platform changes. To provide more dependable, flexible and maintainable real-time systems at a lower cost what is needed is a platform-independent approach to real-time systems development. The development process is composed of two phases: a platform-independent phase, that defines the desired system behaviour and develops a platform-independent design and implementation, and a platform-dependent phase that maps the implementation onto the target platform. The last phase should be highly automated. For critical systems, assessing dependability is crucial. The partitioning into platform dependent and independent phases has to support verification of system properties through both phases.

Real-Time Gene-Expression Extraction with Fast Classification (FC) Networks

Fast Classification (FC) networks were inspired by a biologically plausible mechanism for short term memory where learning occurs instantaneously. Both weights and the topology for an FC network are mapped directly from the training samples by using a prescriptive training scheme. Only two presentations of the training data are required to train an FC network. Compared with iterative learning algorithms such as Back-propagation (which may require many hundreds of presentations of the training data), the training of FC networks is extremely fast and learning convergence is always guaranteed. Thus FC networks may be suitable for applications where real-time classification is needed. In this paper, the FC networks are applied for the real-time extraction of gene expressions for Chlamydia microarray data. Both the classification performance and learning time of the FC networks are compared with the Multi-Layer Proceptron (MLP) networks and support-vector-machines (SVM) in the same classification task. The FC networks are shown to have extremely fast learning time and comparable classification accuracy.

Semantic caching for web-based spatial applications

Client-side caching of spatial data is an important yet very much under investigated issue. Effective caching of vector spatial data has the potential to greatly improve the performance of spatial applications in the Web and wireless environments. In this paper, we study the problem of semantic spatial caching, focusing on effective organization of spatial data and spatial query trimming to take advantage of cached data. Semantic caching for spatial data is a much more complex problem than semantic caching for aspatial data. Several novel ideas are proposed in this paper for spatial applications. A number of typical spatial application scenarios are used to generate spatial query sequences. An extensive experimental performance study is conducted based on these scenarios using real spatial data. We demonstrate a significant performance improvement using our ideas.