Search

Crowdsensing is an emerging paradigm of ubiquitous sensing, through which a crowd of workers are recruited to perform sensing tasks collaboratively. Although it has stimulated many applications, an open fundamental problem is how to select among a massive number of workers to perform a given sensing task under a limited budget. Nevertheless, due to the proliferation of smart devices equipped with various sensors, it is very difficult to profile the workers in terms of sensing ability. Although the uncertainties of the workers can be addressed by conventional Combinatorial Multi-Armed Bandit (CMAB) framework through a trade-off between exploration and exploitation, we do not have sufficient allowance to directly explore and exploit the workers under the limited budget. Furthermore, since the sensor devices usually have quite limited resources, the workers may have bounded capabilities to perform the sensing task only few times, which further restricts our opportunities to learn the uncertainty. To address the above issues, we propose a Context-Aware Worker Selection (CAWS) algorithm in this paper. By leveraging the correlation between the context information of the workers and their sensing abilities, CAWS aims at maximizing the expected cumulative sensing revenue efficiently with both budget constraint and capacity constraints respected, even when the number of the uncertain workers is massive. The efficacy of CAWS can be verified by rigorous theoretical analysis and extensive experiments. [ABSTRACT FROM AUTHOR]

Pathfinding is a fundamental problem for many areas, e.g., robotics, automation, computer-aided design, and computer graphics. Although outdoor pathfinding is fledged, indoor pathfinding remains a challenge due to the lack of indoor maps. Currently, some efforts have utilized building information modeling (BIM) to generate either the grid-based map or the topological map. However, either the grid-based map or the topological map is not sufficient to provide accurate and efficient pathfinding service. This article proposes a novel grid-topological map and develops an accurate and efficient indoor pathfinding scheme based on BIM. The grid-topological map is modeled jointly adopting the advantages of both the grid-based map and the topological map. First, the grid-based map is generated using the BIM data by extracting and mapping geometric and semantic data into planar grids. Second, a grid thinning algorithm is proposed to produce the topological map directly using the grid-based map. Third, a grid-topological map is presented by combining both the grid-based map and topological map. On top of the grid-topological map, an accurate and efficient pathfinding algorithm is developed. Empirical studies proved the effectiveness of the grid-topological map, as well as the accuracy and efficiency of the proposed indoor pathfinding algorithm. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia. Current treatments for AF remain suboptimal due to a lack of understanding of the underlying atrial structures that directly sustain AF. Existing approaches for analyzing atrial structures in 3-D, especially from late gadolinium-enhanced (LGE) magnetic resonance imaging, rely heavily on manual segmentation methods that are extremely labor-intensive and prone to errors. As a result, a robust and automated method for analyzing atrial structures in 3-D is of high interest. We have, therefore, developed AtriaNet, a 16-layer convolutional neural network (CNN), on 154 3-D LGE-MRIs with a spatial resolution of 0.625 mm $\times0.625$ mm $\times1.25$ mm from patients with AF, to automatically segment the left atrial (LA) epicardium and endocardium. AtriaNet consists of a multi-scaled, dual-pathway architecture that captures both the local atrial tissue geometry and the global positional information of LA using 13 successive convolutions and three further convolutions for merging. By utilizing computationally efficient batch prediction, AtriaNet was able to successfully process each 3-D LGE-MRI within 1 min. Furthermore, benchmarking experiments have shown that AtriaNet has outperformed the state-of-the-art CNNs, with a DICE score of 0.940 and 0.942 for the LA epicardium and endocardium, respectively, and an inter-patient variance of <0.001. The estimated LA diameter and volume computed from the automatic segmentations were accurate to within 1.59 mm and 4.01 cm3 of the ground truths. Our proposed CNN was tested on the largest known data set for LA segmentation, and to the best of our knowledge, it is the most robust approach that has ever been developed for segmenting LGE-MRIs. The increased accuracy of atrial reconstruction and analysis could potentially improve the understanding and treatment of AF. [ABSTRACT FROM AUTHOR]

Identification of anonymous identical users of cross-platforms refers to the recognition of the accounts belonging to the same individual among multiple Social Network (SN) platforms. Evidently, cross-platform exploration may help solve many problems in social computing, in both theory and practice. However, it is still an intractable problem due to the fragmentation, inconsistency, and disruption of the accessible information among SNs. Different from the efforts implemented on user profiles and users’ content, many studies have noticed the accessibility and reliability of network structure in most of the SNs for addressing this issue. Although substantial achievements have been made, most of the current network structure-based solutions, requiring prior knowledge of some given identified users, are supervised or semi-supervised. It is laborious to label the prior knowledge manually in some scenarios where prior knowledge is hard to obtain. Noticing that friend relationships are reliable and consistent in different SNs, we proposed an unsupervised scheme, termed Friend Relationship-based User Identification algorithm without Prior knowledge (FRUI-P). The FRUI-P first extracts the friend feature of each user in an SN into friend feature vector, and then calculates the similarities of all the candidate identical users between two SNs. Finally, a one-to-one map scheme is developed to identify the users based on the similarities. Moreover, FRUI-P is proved to be efficient theoretically. Results of extensive experiments demonstrated that FRUI-P performs much better than current state-of-art network structure-based algorithm without prior knowledge. Due to its high precision, FRUI-P can additionally be utilized to generate prior knowledge for supervised and semi-supervised schemes. In applications, the unsupervised anonymous identical user identification method accommodates more scenarios where the seed users are unobtainable. [ABSTRACT FROM PUBLISHER]

Computer models provide a powerful platform for investigating mechanisms that underlie atrial rhythm disturbances. We have used novel techniques to build a structurally-detailed, image-based model of 3-D atrial anatomy. A volume image of the atria from a normal sheep heart was acquired using serial surface macroscopy, then smoothed and down-sampled to 50 \,\mum^3 resolution. Atrial surface geometry was identified and myofiber orientations were estimated throughout by eigen-analysis of the 3-D image structure tensor. Sinus node, crista terminalis, pectinate muscle, Bachman's bundle, and pulmonary veins were segmented on the basis of anatomic characteristics. Heterogeneous electrical properties were assigned to this structure and electrical activation was simulated on it at 100 \mum^3 resolution, using both biophysically-detailed and reduced-order cell activation models with spatially-varying membrane kinetics. We confirmed that the model reproduced key features of the normal spread of atrial activation. Furthermore, we demonstrate that vulnerability to rhythm disturbance caused by structural heterogeneity in the posterior left atrium is exacerbated by spatial variation of repolarization kinetics across this region. These results provide insight into mechanisms that may sustain paroxysmal atrial fibrillation. We conclude that image-based computer models that incorporate realistic descriptions of atrial myofiber architecture and electrophysiologic properties have the potential to analyse and identify complex substrates for atrial fibrillation. [ABSTRACT FROM PUBLISHER]

Micro-computed tomography (micro-CT) has been widely used to generate high-resolution 3-D tissue images from small animals nondestructively, especially for mineralized skeletal tissues. However, its application to the analysis of soft cardiovascular tissues has been limited by poor inter-tissue contrast. Recent ex vivo studies have shown that contrast between muscular and connective tissue in micro-CT images can be enhanced by staining with iodine. In the present study, we apply this novel technique for imaging of cardiovascular structures in canine hearts. We optimize the method to obtain high-resolution X-ray micro-CT images of the canine atria and its distinctive regions—including the Bachmann's bundle, atrioventricular node, pulmonary arteries and veins—with clear inter-tissue contrast. The imaging results are used to reconstruct and segment the detailed 3-D geometry of the atria. Structure tensor analysis shows that the arrangement of atrial fibers can also be characterized using the enhanced micro-CT images, as iodine preferentially accumulates within the muscular fibers rather than in connective tissues. This novel technique can be particularly useful in nondestructive imaging of 3-D cardiac architectures from large animals and humans, due to the combination of relatively high speed (\sim 1 h/per scan of the large canine heart) and high voxel resolution (36 \mum) provided. In summary, contrast micro-CT facilitates fast and nondestructive imaging and segmenting of detailed 3-D cardiovascular geometries, as well as measuring fiber orientation, which are crucial in constructing biophysically detailed computational cardiac models. [ABSTRACT FROM PUBLISHER]