Communication framework to support more effective onsite construction monitoring

The UK construction industry has recently witnessed an increasing demand for cost-reduction strategies due to the strict government regulations on BIM implementation. This adoption will certainly lead to a continuous work improvement, better project delivery and communication. Although the UK government has set a target of 15–20% saving on the costs of capital projects by the full implementation of BIM level 2 in 2016, this figure is unlikely to be met since the majority of construction companies are still spending approximately £20 billion per year on rebuilding and repairing the construction defects caused by miscommunication. This research addresses the problem of communication using traditional methods (i.e. communication through paper-based documents and drawings) and its impact during the construction phase in relation to clash detection. Next, we will present a communication framework using advanced visualisation technique such as augmen ted reality (AR) combined with a BIM model with an easy access to the IFC f ile on site for a compliance checking between the BIM model and the actual co nstruction site. Subsequently, site inspection can be performed more efficiently, and with more reliability. Furthermore, early warning on future occu rring clashes can be given. To reach our objectives, the research has been designed using real case scenario, following two phases of implementation. The first phase include the communication study and consists of determining users requiring a ssistance with regard to site monitoring and inspection, whereas the second, built on the results of the first phase to specify and implement the mobile AR syste

Face hallucination based on nonparametric Bayesian learning

© 2015 IEEE. In this paper, we propose a novel example-based face hallucination method through nonparametric Bayesian learning based on the assumption that human faces have similar local pixel structure. We cluster the low resolution (LR) face image patches by nonparametric method distance dependent Chinese Restaurant process (ddCRP) and calculate the centres of the clusters (i.e., subspaces). Then, we learn the mapping coefficients from the LR patches to high resolution (HR) patches in each subspace. Finally, the HR patches of input low resolution face image can be efficiently generated by a simple linear regression. The spatial distance constraint is employed to aid the learning of subspace centers so that every subspace will better reflect the detailed information of image patches. Experimental results show our method is efficient and promising for face hallucination

Robust dimensionality reduction for human action recognition

Human action recognition can be approached by combining an action-discriminative feature set with a classifier. However, the dimensionality of typical feature sets joint with that of the time dimension often leads to a curse-of-dimensionality situation. Moreover, the measurement of the feature set is subject to sometime severe errors. This paper presents an approach to human action recognition based on robust dimensionality reduction. The observation probabilities of hidden Markov models (HMM) are modelled by mixtures of probabilistic principal components analyzers and mixtures of t-distribution sub-spaces, and compared with conventional Gaussian mixture models. Experimental results on two datasets show that dimensionality reduction helps improve the classification accuracy and that the heavier-tailed t-distribution can help reduce the impact of outliers generated by segmentation errors. © 2010 Crown Copyright

Isotropic reconstruction of 3D fluorescence microscopy images using convolutional neural networks

Fluorescence microscopy images usually show severe anisotropy in axial versus lateral resolution. This hampers downstream processing, i.e. the automatic extraction of quantitative biological data. While deconvolution methods and other techniques to address this problem exist, they are either time consuming to apply or limited in their ability to remove anisotropy. We propose a method to recover isotropic resolution from readily acquired anisotropic data. We achieve this using a convolutional neural network that is trained end-to-end from the same anisotropic body of data we later apply the network to. The network effectively learns to restore the full isotropic resolution by restoring the image under a trained, sample specific image prior. We apply our method to $3$ synthetic and $3$ real datasets and show that our results improve on results from deconvolution and state-of-the-art super-resolution techniques. Finally, we demonstrate that a standard 3D segmentation pipeline performs on the output of our network with comparable accuracy as on the full isotropic data

Cloud service-oriented dashboard for work cell management in RFID-enabled ubiquitous manufacturing

This article aims at developing a service-oriented dashboard for operators and supervisors of manufacturing shopfloor work-cells to realize information visibility and traceability effectively with cloud and RFID (radio frequency identification) technologies. The work is based on a case of an illustrative assembly line consisting of a number of work cells. The dashboard is deployed for facilitating assembly operations in ubiquitous manufacturing environment. The utilization of the system leads to significant improvements in work cell productivity and quality, operational flexibility and decision efficiency. © 2013 IEEE.published_or_final_versio

Compressive Sensing of time series for human action recognition

Compressive Sensing (CS) is an emerging signal processing technique where a sparse signal is reconstructed from a small set of random projections. In the recent literature, CS techniques have demonstrated promising results for signal compression and reconstruction [9, 8, 1]. However, their potential as dimensionality reduction techniques for time series has not been significantly explored to date. To this aim, this work investigates the suitability of compressive-sensed time series in an application of human action recognition. In the paper, results from several experiments are presented: (1) in a first set of experiments, the time series are transformed into the CS domain and fed into a hidden Markov model (HMM) for action recognition; (2) in a second set of experiments, the time series are explicitly reconstructed after CS compression and then used for recognition; (3) in the third set of experiments, the time series are compressed by a hybrid CS-Haar basis prior to input into HMM; (4) in the fourth set, the time series are reconstructed from the hybrid CS-Haar basis and used for recognition. We further compare these approaches with alternative techniques such as sub-sampling and filtering. Results from our experiments show unequivocally that the application of CS does not degrade the recognition accuracy; rather, it often increases it. This proves that CS can provide a desirable form of dimensionality reduction in pattern recognition over time series. © 2010 Crown Copyright

Entropy on Spin Factors

Recently it has been demonstrated that the Shannon entropy or the von Neuman entropy are the only entropy functions that generate a local Bregman divergences as long as the state space has rank 3 or higher. In this paper we will study the properties of Bregman divergences for convex bodies of rank 2. The two most important convex bodies of rank 2 can be identified with the bit and the qubit. We demonstrate that if a convex body of rank 2 has a Bregman divergence that satisfies sufficiency then the convex body is spectral and if the Bregman divergence is monotone then the convex body has the shape of a ball. A ball can be represented as the state space of a spin factor, which is the most simple type of Jordan algebra. We also study the existence of recovery maps for Bregman divergences on spin factors. In general the convex bodies of rank 2 appear as faces of state spaces of higher rank. Therefore our results give strong restrictions on which convex bodies could be the state space of a physical system with a well-behaved entropy function.Comment: 30 pages, 6 figure

Current profiles and AC losses of a superconducting strip with elliptic cross-section in perpendicular magnetic field

The case of a hard type II superconductor in the form of strip with elliptic cross-section when placed in transverse magnetic field is studied. We approach the problem in two steps, both based on the critical-state model. First we calculate numerically the penetrated current profiles that ensure complete shielding in the interior, without assuming an a priori form for the profiles. In the second step we introduce an analytical approximation that asumes that the current profiles are ellipses. Expressions linking the sample magnetization to the applied field are derived covering the whole range of applied fields. The theoretical predictions are tested by the comparison with experimental data for the imaginary part of AC susceptibility.Comment: 12 pages; 3 figure

LNK (SH2B3): paradoxical effects in ovarian cancer.

LNK (SH2B3) is an adaptor protein studied extensively in normal and malignant hematopoietic cells. In these cells, it downregulates activated tyrosine kinases at the cell surface resulting in an antiproliferative effect. To date, no studies have examined activities of LNK in solid tumors. In this study, we found by in silico analysis and staining tissue arrays that the levels of LNK expression were elevated in high-grade ovarian cancer. To test the functional importance of this observation, LNK was either overexpressed or silenced in several ovarian cancer cell lines. Remarkably, overexpression of LNK rendered the cells resistant to death induced by either serum starvation or nutrient deprivation, and generated larger tumors using a murine xenograft model. In contrast, silencing of LNK decreased ovarian cancer cell growth in vitro and in vivo. Western blot studies indicated that overexpression of LNK upregulated and extended the transduction of the mitogenic signal, whereas silencing of LNK produced the opposite effects. Furthermore, forced expression of LNK reduced cell size, inhibited cell migration and markedly enhanced cell adhesion. Liquid chromatography-mass spectroscopy identified 14-3-3 as one of the LNK-binding partners. Our results suggest that in contrast to the findings in hematologic malignancies, the adaptor protein LNK acts as a positive signal transduction modulator in ovarian cancers