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Most digital cameras are overlaid with color filter arrays (CFA) on their electronic sensors, and thus only one particular color value would be captured at every pixel location. When producing the output image, one needs to recover the full color image from such incomplete color samples, and this process is known as demosaicking. In this paper, we propose a novel context-constrained demosaicking algorithm via sparse-representation based joint dictionary learning. Given a single mosaicked image with incomplete color samples, we perform color and texture constrained image segmentation and learn a dictionary with different context categories. A joint sparse representation is employed on different image components for predicting the missing color information in the resulting high-resolution image. During the dictionary learning and sparse coding processes, we advocate a locality constraint in our algorithm, which allows us to locate most relevant image data and thus achieve improved demosaicking performance. Experimental results show that the proposed method outperforms several existing or state-of-the-art techniques in terms of both subjective and objective evaluations.

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Application paradigms will increasingly exceed a mobile device's physical boundaries. This paper presents a system solution for a mobile device to mount remote sensors on other devices. Our design is generic to mobile senor stacks, thus supporting unmodified apps and commodity sensors. Furthermore, it uses an asynchronous access model to facilitate semantics passing and data reporting in between. Such semantic information allows the development of an energy-efficient reporting policy for remote sensing applications. The results of experiments conducted on commercial Android smartphones with popular apps demonstrate that our design is very efficient in terms of energy consumption and completion time.

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Resident applications, which autonomously awaken mobile devices, can gradually and imperceptibly drain device batteries. This paper introduces the concept of alarm similarity into wakeup management for mobile systems in connected standby. First, we define hardware similarity to reflect the degree of energy savings and time similarity to reflect the impact on user experience. We then propose a policy that aligns alarms based on their similarity to save standby energy while maintaining the quality of the user experience. Finally, we integrate our design into Android and conduct extensive experiments on a commercial smartphone running popular mobile apps. The results demonstrate that our design can further extend the standby time achieved with Android's native policy by up to one-third.

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Visual summarization addresses the task of selecting images from an image collection, so that the sampled images would contain representative information which sufficiently highlights the collected visual data. In this paper, we solve the problem of style-centric visual summarization using photographic landmark images of a city. Different from existing works which typically retrieve landmark images based on salient visual appearances, our proposed method is able to produce different sets of summarized images, while each set corresponds to a particular image style. This is achieved by performing unsupervised clustering on images within and across landmark categories, which discovers the common photographic styles from the input image collection. Our experiments will confirm that, compared to standard clustering algorithms, our approach is able to achieve satisfactory summarization outputs with style consistency.

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This paper considers a two-cell uplink cochannel multiple-input multiple-output (MIMO) network with users sequentially arriving to the network. We study the problem of sequential base station (BS) selection for the users, with the selection criterion based on the degrees of freedom (DoF) available for the new arriving user. We find that different sequential BS selections affect individual and network performance in terms of the individual and network sum DoF as well as the number of admissible users in the network. We propose a method to build the tree structure for sequential BS selection, which carries trellis information for individual and system-wide selections. The properties of the tree are analytically studied. It turns out that by adopting an interference coordination strategy based on the concept of interference alignment, a better individual and network performance can be achieved. Simulation compares the proposed DoF-based BS selection and traditional BS selection schemes and highlights the advantages of the proposed scheme.

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Mobile applications will become progressively more complicated and diverse. Heterogeneous computing architectures like big.LITTLE are a hardware solution that allows mobile devices to combine computing performance and energy efficiency. However, software solutions that conform to the paradigm of conventional fair scheduling and governing are not applicable to mobile systems, thereby degrading user experience or reducing energy efficiency. In this article, we exploit the concept of application sensitivity, which reflects the user’s attention on each application, and devise a user-centric scheduler and governor that allocate computing resources to applications according to their sensitivity. Furthermore, we integrate our design into the Android operating system. The results of experiments conducted on a commercial big.LITTLE smartphone with real-world mobile apps demonstrate that the proposed design can achieve significant gains in energy efficiency while improving the quality of user experience.

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