ALBERT

Description: Sensors, Vol. 18, Pages 2858: Joint Source and Channel Rate Allocation over Noisy Channels in a Vehicle Tracking Multimedia Internet of Things System Sensors doi: 10.3390/s18092858 Authors: Yixin Mei Fan Li Lijun He Liejun Wang As an emerging type of Internet of Things (IoT), multimedia IoT (MIoT) has been widely used in the domains of healthcare, smart buildings/homes, transportation and surveillance. In the mobile surveillance system for vehicle tracking, multiple mobile camera nodes capture and upload videos to a cloud server to track the target. Due to the random distribution and mobility of camera nodes, wireless networks are chosen for video transmission. However, the tracking precision can be decreased because of degradation of video quality caused by limited wireless transmission resources and transmission errors. In this paper, we propose a joint source and channel rate allocation scheme to optimize the performance of vehicle tracking in cloud servers. The proposed scheme considers the video content features that impact tracking precision for optimal rate allocation. To improve the reliability of data transmission and the real-time video communication, forward error correction is adopted in the application layer. Extensive experiments are conducted on videos from the Object Tracking Benchmark using the H.264/AVC standard and a kernelized correlation filter tracking scheme. The results show that the proposed scheme can allocate rates efficiently and provide high quality tracking service under the total transmission rate constraints.

Description: IJERPH, Vol. 15, Pages 1075: Reduced Recovery of Depression in Female T Allele Carriers of TNF-RII rs1061622 at Earlier Stage after Wenchuan Earthquake International Journal of Environmental Research and Public Health doi: 10.3390/ijerph15061075 Authors: Nazakat Hussain Memon Mei Fan Jia Lin Yan Jun Si Mi Su Qi Wei Guo Ding Zhi Fang Objective: The aim of current study was to explore longitudinally the prevalence, severity, potential factors, and predictors of depression among Chinese Han adolescent survivors with different genotypes of tumor necrosis factor receptor-II (TNF-RII) rs1061622 after the 2008 Wenchuan earthquake. Method: TNF-RII rs1061622 variants were examined by polymerase chain reaction–restriction fragment length polymorphism and verified by DNA sequencing. Depression symptoms were assessed by Beck Depression Inventory (BDI) among 439 high school students at 6, 12, and 18 months after the earthquake. Results: No significant differences were observed in depression prevalence and BDI scores between the TT homozygotes and the G allele carriers in both the male and female subjects. However, the female TT homozygotes had a higher depression prevalence than the male TT homozygotes at 6, 12, and 18 months, whereas the female G allele carriers had a higher depression prevalence than the male G allele carriers only at 6 and 12 months after the earthquake. Moreover, BDI scores declined in the male subjects with both genotypes and only in the female G allele carriers at 12 months when compared with those at 6 months. Furthermore, the predictors of depression severity or potential factors of depression prevalence were different between the G allele carriers and the TT homozygotes at different times after the earthquake. Conclusion: It is concluded that the association of TNF-RII rs1061622 with depression is longitudinally different in Chinese Han adolescents after the 2008 Wenchuan earthquake. The T allele may be associated with reduced recovery of depression in female adolescents in the earlier stage of depression rehabilitation.

Description: Remote Sensing, Vol. 9, Pages 1166: Sparse Unmixing of Hyperspectral Data with Noise Level Estimation Remote Sensing doi: 10.3390/rs9111166 Authors: Chang Li Yong Ma Xiaoguang Mei Fan Fan Jun Huang Jiayi Ma Recently, sparse unmixing has received particular attention in the analysis of hyperspectral images (HSIs). However, traditional sparse unmixing ignores the different noise levels in different bands of HSIs, making such methods sensitive to different noise levels. To overcome this problem, the noise levels at different bands are assumed to be different in this paper, and a general sparse unmixing method based on noise level estimation (SU-NLE) under the sparse regression framework is proposed. First, the noise in each band is estimated on the basis of the multiple regression theory in hyperspectral applications, given that neighboring spectral bands are usually highly correlated. Second, the noise weighting matrix can be obtained from the estimated noise. Third, the noise weighting matrix is integrated into the sparse regression unmixing framework, which can alleviate the impact of different noise levels at different bands. Finally, the proposed SU-NLE is solved by the alternative direction method of multipliers. Experiments on synthetic datasets show that the signal-to-reconstruction error of the proposed SU-NLE is considerably higher than those of the corresponding traditional sparse regression unmixing methods without noise level estimation, which demonstrates the efficiency of integrating noise level estimation into the sparse regression unmixing framework. The proposed SU-NLE also shows promising results in real HSIs.