Data fusion approaches and applications for construction engineering

Data fusion can be defined as the process of combining data or information for estimating the state of an entity. Data fusion is a multidisciplinary field that has several benefits, such as enhancing the confidence, improving reliability, and reducing ambiguity of measurements for estimating the state of entities in engineering systems. It can also enhance completeness of fused data that may be required for estimating the state of engineering systems. Data fusion has been applied to different fields, such as robotics, automation, and intelligent systems. This paper reviews some examples of recent applications of data fusion in civil engineering and presents some of the potential benefits of using data fusion in civil engineering.

Atmospheric turbulence mitigation using complex wavelet-based fusion

Restoring a scene distorted by atmospheric turbulence is a challenging problem in video surveillance. The effect, caused by random, spatially varying, perturbations, makes a model-based solution difficult and in most cases, impractical. In this paper, we propose a novel method for mitigating the effects of atmospheric distortion on observed images, particularly airborne turbulence which can severely degrade a region of interest (ROI). In order to extract accurate detail about objects behind the distorting layer, a simple and efficient frame selection method is proposed to select informative ROIs only from good-quality frames. The ROIs in each frame are then registered to further reduce offsets and distortions. We solve the space-varying distortion problem using region-level fusion based on the dual tree complex wavelet transform. Finally, contrast enhancement is applied. We further propose a learning-based metric specifically for image quality assessment in the presence of atmospheric distortion. This is capable of estimating quality in both full-and no-reference scenarios. The proposed method is shown to significantly outperform existing methods, providing enhanced situational awareness in a range of surveillance scenarios. © 1992-2012 IEEE.

FB1, an E2A fusion partner in childhood leukemia, interacts with U19/EAF2 and inhibits its transcriptional activity

Background: U19/EAF2 is a potential tumor suppressor exhibiting frequent down-regulation and allelic loss in advanced human prostate cancer specimens. U 19/EAF2 has also been identified as ELL-associated factor 2 (EAF2) based on its binding to ELL, a fusion partner of MLL in acute myeloid leukemia. U19/EAF2 is a putative transcription factor with a transactivation domain and capability of sequence-specific DNA binding. Methods: Yeast-two-hybrid-screening was used to identify U19/EAF2-binding partners. Co-immunoprecipitation and mammalian 1-hybrid assay were used to characterize a U19/EAF2-binding partner. Results: FB1, an E2A fusion partner in childhood leukemia, was identified as a binding-partner of U19/EAF2. FB1 also binds to EAF1, the only homologue of U19/EAF2. FB1 also interacts and co-localizes with ELL in the nucleus. Interestingly, FB1 inhibited the transcriptional activity of U19/EAF2 but not EAF1. Conclusions: FB1 is an important binding partner and a functional regulator of U19/EAF2, EAF1, and/or ELL. (c) 2007 Elsevier Ireland Ltd. All rights reserved.

Ground target detection, classification and sensor fusion in distributed fiber seismic sensor network - art. no. 683015

This paper describes the ground target detection, classification and sensor fusion problems in distributed fiber seismic sensor network. Compared with conventional piezoelectric seismic sensor used in UGS, fiber optic sensor has advantages of high sensitivity and resistance to electromagnetic disturbance. We have developed a fiber seismic sensor network for target detection and classification. However, ground target recognition based on seismic sensor is a very challenging problem because of the non-stationary characteristic of seismic signal and complicated real life application environment. To solve these difficulties, we study robust feature extraction and classification algorithms adapted to fiber sensor network. An united multi-feature (UMF) method is used. An adaptive threshold detection algorithm is proposed to minimize the false alarm rate. Three kinds of targets comprise personnel, wheeled vehicle and tracked vehicle are concerned in the system. The classification simulation result shows that the SVM classifier outperforms the GMM and BPNN. The sensor fusion method based on D-S evidence theory is discussed to fully utilize information of fiber sensor array and improve overall performance of the system. A field experiment is organized to test the performance of fiber sensor network and gather real signal of targets for classification testing.

Recombinant protein of heptad-repeat HR212, a stable fusion inhibitor with potent anti-HIV action in vitro

HR212, a recombinant protein expressed in Escherichia coli, has been previously reported to inhibit HIV-1 membrane fusion at low nanomolar level. Here we report that HR212 is effective in blocking laboratory strain HIV-1IIIB entry and replication with EC50 values of 3.92±0.62 and 6.59±1.74 nM, respectively, and inhibiting infection by clinic isolate HIV-1KM018 with EC50 values of 44.44±10.20 nM, as well as suppressing HIV-1- induced cytopathic effect with an EC50 value of 3.04±1.20 nM. It also inhibited HIV-2ROD and HIV-2CBL-20 entry and replication in the μM range. Notably, HR212 was highly effective against T20-resistant strains with EC50 values ranging from 5.09 to 7.75 nM. Unlike T20, HR212 showed stability sufficient to inhibit syncytia formation in a time-of-addition assay, and was insensitive to proteinase K digestion. These results suggest that HR212 has great potential to be further developed as novel HIV-1 fusion inhibitor for treatment of HIV/ AIDS patients, particularly for those infected by T20-resistant variants.

Dynamics of morphological changes for mitochondrial fission and fusion

Mitochondria experience continuous fusion and fission in a living cell, but their dynamics remains poorly quantified. Here a theoretical model was developed, upon a simplified population balance equation (PBE), to predict the morphological changes induced by mitochondrial fission and fusion. Assuming that both fission and fusion events are statistically independent, the survival probability of mitochondria staying in the fission or fusion state was formulated as an exponentially-decayed function with time, which depended on the time-dependent distribution of the mitochondrial volume and the fission and fusion rates. Parametric analysis was done for two typical volume distributions. One was Gamma distribution and the other was Gaussian distribution, derived from the measurements of volume distribution for individual mitochondria in a living cell and purified mitochondria in vitro. The predictions indicated that the survival probability strongly depended on morphological changes of individual mitochondria and was inversely correlated to the fission and fusion rates. This work provided a new insight into quantifying the mitochondrial dynamics via monitoring the evolution of the mitochondrial volume.