ieee access special section editorial emotion aware mobile computing

With the rapid development of smart phones and wireless technology, mobile services and applications in the world are growing rapidly. Advanced mobile computing and communications greatly enhance users' experience by the notion of "carrying small while enjoying large", which have brought a huge impact to all aspects of people's lifestyles in terms of work, social, and economy. Although these advanced techniques have extensively improved users' quality of experience (QoE), it is not adequate to provide affective services without efficient mechanisms of emotion-aware mobile computing, which includes various unique aspects, e.g., mobile data sensing and transmissions; sentiment analysis and emotion recognition; affective interaction. Under the new service paradigm, novel mobile services and innovative applications need to be extensively investigated to gain the great potentials brought by emotion-aware mobile computing

Mathematical Approaches for Image Enhancement Problems

This thesis develops novel techniques that can solve some image enhancement problems using theoretically and technically proven and very useful mathematical tools to image processing such as wavelet transforms, partial differential equations, and variational models. Three subtopics are mainly covered. First, color image denoising framework is introduced to achieve high quality denoising results by considering correlations between color components while existing denoising approaches can be plugged in flexibly. Second, a new and efficient framework for image contrast and color enhancement in the compressed wavelet domain is proposed. The proposed approach is capable of enhancing both global and local contrast and brightness as well as preserving color consistency. The framework does not require inverse transform for image enhancement since linear scale factors are directly applied to both scaling and wavelet coefficients in the compressed domain, which results in high computational efficiency. Also contaminated noise in the image can be efficiently reduced by introducing wavelet shrinkage terms adaptively in different scales. The proposed method is able to enhance a wavelet-coded image computationally efficiently with high image quality and less noise or other artifact. The experimental results show that the proposed method produces encouraging results both visually and numerically compared to some existing approaches. Finally, image inpainting problem is discussed. Literature review, psychological analysis, and challenges on image inpainting problem and related topics are described. An inpainting algorithm using energy minimization and texture mapping is proposed. Mumford-Shah energy minimization model detects and preserves edges in the inpainting domain by detecting both the main structure and the detailed edges. This approach utilizes faster hierarchical level set method and guarantees convergence independent of initial conditions. The estimated segmentation results in the inpainting domain are stored in segmentation map, which is referred by a texture mapping algorithm for filling textured regions. We also propose an inpainting algorithm using wavelet transform that can expect better global structure estimation of the unknown region in addition to shape and texture properties since wavelet transforms have been used for various image analysis problems due to its nice multi-resolution properties and decoupling characteristics

l0 Sparse signal processing and model selection with applications

Sparse signal processing has far-reaching applications including compressed sensing, media compression/denoising/deblurring, microarray analysis and medical imaging. The main reason for its popularity is that many signals have a sparse representation given that the basis is suitably selected. However the difficulty lies in developing an efficient method of recovering such a representation. To this aim, two efficient sparse signal recovery algorithms are developed in the first part of this thesis. The first method is based on direct minimization of the l0 norm via cyclic descent, which is called the L0LS-CD (l0 penalized least squares via cyclic descent) algorithm. The other method minimizes smooth approximations of sparsity measures including those of the l0 norm via the majorization minimization (MM) technique, which is called the QC (quadratic concave) algorithm. The L0LS-CD algorithm is developed further by extending it to its multivariate (V-L0LS-CD (vector L0LS-CD)) and group (gL0LS-CD (group L0LS-CD)) regression variants. Computational speed-ups to the basic cyclic descent algorithm are discussed and a greedy version of L0LS-CD is developed. Stability of these algorithms is analyzed and the impact of the penalty parameter and proper initialization on the algorithm performance are highlighted. A suitable method for performance comparison of sparse approximating algorithms in the presence of noise is established. Simulations compare L0LS-CD and V-L0LS-CD with a range of alternatives on under-determined as well as over-determined systems. The QC algorithm is applicable to a class of penalties that are neither convex nor concave but have what we call the quadratic concave property. Convergence proofs of this algorithm are presented and it is compared with the Newton algorithm, concave convex (CC) procedure, as well as with the class of proximity algorithms. Simulations focus on the smooth approximations of the l0 norm and compare them with other l0 denoising algorithms. Next, two applications of sparse modeling are considered. In the first application the L0LS-CD algorithm is extended to recover a sparse transfer function in the presence of coloured noise. The second uses gL0LS-CD to recover the topology of a sparsely connected network of dynamic systems. Both applications use Laguerre basis functions for model expansion. The role of model selection in sparse signal processing is widely neglected in literature. The tuning/penalty parameter of a sparse approximating problem should be selected using a model selection criterion which minimizes a desired discrepancy measure. Compared to the commonly used model selection methods, the SURE (Stein's unbiased risk estimator) estimator stands out as one which does not suffer from the limitations of other methods. Most model selection criterion are developed based on signal or prediction mean squared error. The last section of this thesis develops an SURE criterion instead for parameter mean square error and applies this result to l1 penalized least squares problem with grouped variables. Simulations based on topology identification of a sparse network are presented to illustrate and compare with alternative model selection criteria

Myth and Symbol II: Symbolic phenomena in Ancient Greek culture.

Papers from the second and third international symposia on symbolism at The Norwegian institute at Athens, September 21-24, 2000 and September 19-22,200