Three-dimensional tissue scaffolds from interbonded poly(e-caprolactone) fibrous matrices with controlled porosity

In this article, we report on the preparation and cell culture performance of a novel fibrous matrix that has an interbonded fiber architecture, excellent pore interconnectivity, and controlled pore size and porosity. The fibrous matrices were prepared by combining melt-bonding of short synthetic fibers with a template leaching technique. The microcomputed tomography and scanning electron microscopy imaging verified that the fibers in the matrix were highly bonded, forming unique isotropic pore architectures. The average pore size and porosity of the fibrous matrices were controlled by the fiber/template ratio. The matrices having the average pore size of 120, 207, 813, and 994 mm, with the respective porosity of 73%, 88%, 96%, and 97%, were investigated. The applicability of the matrix as a three-dimensional (3D) tissue scaffold for cell culture was demonstrated with two cell lines, rat skin fibroblast and Chinese hamster ovary, and the influences of the matrix porosity and surface area on the cell culture performance were examined. Both cell lines grew successfully in the matrices, but they showed different preferences in pore size and porosity. Compared with two-dimensional tissue culture plates, the cell number on 3D fibrous matrices was increased by 97.27% for the Chinese hamster ovary cells and 49.46% for the fibroblasts after 21 days of culture. The fibroblasts in the matrices not only grew along the fiber surface but also bridged among the fibers, which was much different from those on two-dimensional scaffolds. Such an interbonded fibrous matrix may be useful for developing new fiber-based 3D tissue scaffolds for various cell culture applications.

Inter-bonded fibrous matrices for 3D tissue engineering scaffolds

The thesis established a stable three-dimensional fibrous tissue scaffold that has controlled pore structure and inter-bonded fibrous structure, and also examined the effects of the 3D fibrous matrices and functional surfaces including nano-scale topography, bioactive CaP coating and antibacterial treatment on the cell growth behavior for tissue engineering application.

3D sparse feature model using short baseline stereo and multiple view registration

This paper outlines a methodology to generate a distinctive object representation offline, using short-baseline stereo fundamentals to triangulate highly descriptive object features in multiple pairs of stereo images. A group of sparse 2.5D perspective views are built and the multiple views are then fused into a single sparse 3D model using a common 3D shape registration technique. Having prior knowledge, such as the proposed sparse feature model, is useful when detecting an object and estimating its pose for real-time systems like augmented reality.

Computable social patterns from sparse sensor data

We present a computational framework to automatically discover high-order temporal social patterns from very noisy and sparse location data. We introduce the concept of social footprint and present a method to construct a codebook, enabling the transformation of raw sensor data into a collection of social pages. Each page captures social activities of a user over regular time period, and represented as a sequence of encoded footprints. Computable patterns are then defined as repeated structures found in these sequences. To do so, we appeal to modeling tools in document analysis and propose a Latent Social theme Dirichlet Allocation (LSDA) model - a version of the Ngram topic model in [6] with extra modeling of personal context. This model can be viewed as a Bayesian clustering method, jointly discovering temporal collocation of footprints and exploiting statistical strength across social pages, to automatically discovery high-order patterns. Alternatively, it can be viewed as a dimensionality reduction method where the reduced latent space can be interpreted as the hidden social 'theme' - a more abstract perception of user's daily activities. Applying this framework to a real-world noisy dataset collected over 1.5 years, we show that many useful and interesting patterns can be computed. Interpretable social themes can also be deduced from the discovered patterns.

Generation of 3D sparse feature models using multiple stereo views

Augmented Reality (AR) renders virtual information onto objects in the real world. This new user interface paradigm presents a seamless blend of the virtual and real, where the convergence of the two is difficult to discern. However, errors in the registration of the real and virtual worlds are common and often destroy the AR illusion. To achieve accurate and efficient registration, the pose of real objects must be resolved in a quick and precise manner.

Nonnegative blind source separation by sparse component analysis based on determinant measure

The problem of nonnegative blind source separation (NBSS) is addressed in this paper, where both the sources and the mixing matrix are nonnegative. Because many real-world signals are sparse, we deal with NBSS by sparse component analysis. First, a determinant-based sparseness measure, named D-measure, is introduced to gauge the temporal and spatial sparseness of signals. Based on this measure, a new NBSS model is derived, and an iterative sparseness maximization (ISM) approach is proposed to solve this model. In the ISM approach, the NBSS problem can be cast into row-to-row optimizations with respect to the unmixing matrix, and then the quadratic programming (QP) technique is used to optimize each row. Furthermore, we analyze the source identifiability and the computational complexity of the proposed ISM-QP method. The new method requires relatively weak conditions on the sources and the mixing matrix, has high computational efficiency, and is easy to implement. Simulation results demonstrate the effectiveness of our method.

Sparse subspace representation for spectral document clustering

We present a novel method for document clustering using sparse representation of documents in conjunction with spectral clustering. An ℓ1-norm optimization formulation is posed to learn the sparse representation of each document, allowing us to characterize the affinity between documents by considering the overall information instead of traditional pair wise similarities. This document affinity is encoded through a graph on which spectral clustering is performed. The decomposition into multiple subspaces allows documents to be part of a sub-group that shares a smaller set of similar vocabulary, thus allowing for cleaner clusters. Extensive experimental evaluations on two real-world datasets from Reuters-21578 and 20Newsgroup corpora show that our proposed method consistently outperforms state-of-the-art algorithms. Significantly, the performance improvement over other methods is prominent for this datasets.

Object categorization via sparse representation of local features

Sparse representation has been introduced to address many recognition problems in computer vision. In this paper, we propose a new framework for object categorization based on sparse representation of local features. Unlike most of previous sparse coding based methods in object classification that only use sparse coding to extract high-level features, the proposed method incorporates sparse representation and classification into a unified framework. Therefore, it does not need a further classifier. Experimental results show that the proposed method achieved better or comparable accuracy than the well known bag-of-features representation with various classifiers.