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Abstract-The rapidly increasing power of personal mobile devices (smartphones, tablets, etc.) is providing much richer contents and social interactions to users on the move. This trend however is throttled by the limited battery lifetime of mobile devices and unstable wireless connectivity, making the highest possible quality of service experienced by mobile users not feasible. The recent cloud computing technology, with its rich resources to compensate for the limitations of mobile devices and connections, can potentially provide an ideal platform to support the desired mobile services. Tough challenges arise on how to effectively exploit cloud resources to facilitate mobile services, especially those with stringent interaction delay requirements. In this paper, we propose the design of a Cloud-based, novel Mobile sOcial tV system (CloudMoV). The system effectively utilizes both PaaS (Platform-as-a-Service) and IaaS (Infrastructure-as-a-Service) cloud services to offer the living-room experience of video watching to a group of disparate mobile users who can interact socially while sharing the video. To guarantee good streaming quality as experienced by the mobile users with time-varying wireless connectivity, we employ a surrogate for each user in the IaaS cloud for video downloading and social exchanges on behalf of the user. The surrogate performs efficient stream transcoding that matches the current connectivity quality of the mobile user. Given the battery life as a key performance bottleneck, we advocate the use of burst transmission from the surrogates to the mobile users, and carefully decide the burst size which can lead to high energy efficiency and streaming quality. Social interactions among the users, in terms of spontaneous textual exchanges, are effectively achieved by efficient designs of data storage with BigTable and dynamic handling of large volumes of concurrent messages in a typical PaaS cloud. These various designs for flexible transcoding capabilities, battery efficiency of mobile devices and spontaneous social interactivity together provide an ideal platform for mobile social TV services. We have implemented CloudMoV on Amazon EC2 and Google App Engine and verified its superior performance based on real-world experiments.

In this paper, we propose a new approach to recognize the motional patterns of human postures by introducing the data density functional method. Under the framework of the proposed method, sensed time signals will be mapped into specific physical spaces. The most probable cluster number within the specific physical space can be determined according to the principle of energy stability. Then, each corresponding cluster boundary can be measured by searching for the local lowest energy level. Finally, the configuration of the clusters in the space will characterize the most probable states of the motional patterns. The direction of state migration and the corresponding transition region between these states then constitute a significant motional feature in the specific space. Differing from conventional methods, only a single tri-axial gravitational sensor was employed for data acquirement in our hardware scheme. By combining the motional feature and the sensor architecture as prior information, experimental results verified that the most probable states of the motional patterns can be successfully classified into four common human postures of daily life. Furthermore, error motions and noise only offer insignificant influences. Eventually, the proposed approach was applied on a simulation of turning-over situations, and the results show its potential on the issue of elderly and infant turning-over monitoring.

MoS2 quantum dots (QDs) functionalized g-C3N4 nanosheets (MoS2@CNNS) were prepared through a protonation-assisted ion exchange method, which were developed as a highly efficient biomimetic catalyst. Structural analysis revealed that uniformly-dispersed MoS2 QDs with controllable size and different loading amount grew in-situ on the surface of CNNS, forming close-contact MoS2@CNNS nanostructures and exhibiting distinct surface properties. Compared to MoS2 QDs and CNNS, the MoS2@CNNS nanocomposites exhibited a more than four times stronger peroxidase-like catalytic activity, which could catalyze the oxidation of 3,3’,5,5’-tetramethylbenzidine (TMB) in the presence of H2O2 to generate a blue oxide. Among the MoS2@CNNS nanocomposites, MoS2@CNNS(30) was verified to present the best intrinsic peroxidase-like performance, which could be attributed to the more negative potential and larger specific surface area. A simple, rapid and ultrasensitive system for colorimetric detection of H2O2 was thus successfully established based on MoS2@CNNS, displaying nice selectivity, reusability, and stability. The detection limit of H2O2 could reach as low as 0.02 μM. Furthermore, the kinetic and active species trapping experiments indicated the peroxidase-like catalytic mechanism of MoS2@CNNS. This work develops a novel, rapid, and ultrasensitive approach for visual assay of H2O2, which has a potential application prospect on clinical diagnosis and biomedical analysis.