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Background The Pittsburgh Fatigability Scale (PFS) was developed to capture fatigue and demand in a single tool, filling a gap that no validated questionnaire existed to measure perceived fatigability. Since fatigability is a more sensitive measure of a person’s susceptibility to fatigue, we validated the simplified-Chinese version of the PFS among Chinese community-dwelling older adults. Methods This cross-sectional study was conducted in an urban community in Beijing between November 2018 and July 2019. The PFS was translated into simplified-Chinese by the translation, retro-translation method. Internal consistency of the Physical subscale of the PFS was evaluated by Cronbach’s alpha. Convergent validity and discriminant validity were evaluated against physical performance measures (i.e., Short Physical Performance Battery & Timed Up and Go Test) and daily living performance (i.e., Barthel Index & Instrumental activity of daily living). Results Our study included 457 participants, including 182 men (39.8%) and 275 women (60.2%). The age range of the included participants was 61–96 years (mean = 84.8 years, SD = 5.8 years). The simplified-Chinese version of PFS Physical scores showed strong internal consistency (Cronbach’s alpha = 0.81). Higher PFS Physical scores were associated with worse physical performance, and daily living performance (|correlation coefficient| range: 0.36–0.56, p p Conclusions The PFS simplified-Chinese version is a valid instrument to assess perceived physical fatigability in Chinese-speaking older adults with good convergent validity. Thus, the PFS, with low cost and greater feasibility, is a desired tool to measure fatigability in large population studies.

Background: The Pittsburgh Fatigability Scale (PFS) was developed to capture fatigue and demand in a single tool, filling a gap that no validated questionnaire existed to measure perceived fatigability. Since fatigability is a more sensitive measure of a person’s susceptibility to fatigue, we validated the simplified-Chinese version of the PFS among Chinese community-dwelling older adults.Methods: The PFS was translated into the simplified-Chinese by the translation, retro-translation method. Internal consistency of the Physical subscale of the PFS was evaluated by Cronbach’s alpha. Convergent validity and discriminant validity were evaluated against physical performance measures (i.e., Short Physical Performance Battery & Timed Up and Go Test) and daily living performance (i.e., Barthel Index & Instrumental activity of daily living).Results: The simplified-Chinese version of PFS Physical scores showed strong internal consistency (Cronbach’s alpha=0.81). Higher PFS Physical scores were associated with worse physical performance, and daily living performance (|correlation coefficient| range: 0.36-0.56, pConclusions: The PFS simplified-Chinese version is a valid instrument to assess perceived physical fatigability in Chinese-speaking older adults with good convergent validity. Thus, the PFS, with low cost and greater feasibility, is a desired tool to measure fatigability in large population studies.

With the continuous improvement of the social and economic level, the number of vehicles has exploded in the city, and traditional manual identification license plates have been unable to meet the demand. In this paper, a Convolutional neural network (CNN)-based license plate recognition system is designed. The recognition module uses the CNN+LSTM+CTC model to simplify the convolutional layer structure to adapt to the lightweight training mode. The two-way LSTM structure is used to learn from both sides of the license plate to enhance the end-to-end recognition effect. Compared with the traditional scheme, the CTC loss calculation method eliminates the need for character alignment, streamlines the steps, and improves the recognition accuracy. The experiment shows that the license plate recognition software system designed in this paper has a high recognition accuracy rate of 98.59%.

The application of electroencephalogram (EEG) generated by human viewing images is a new thrust in image retrieval technology. A P300 component in the EEG is induced when the subjects see their point of interest in a target image under the rapid serial visual presentation (RSVP) experimental paradigm. We detected the single-trial P300 component to determine whether a subject was interested in an image. In practice, the latency and amplitude of the P300 component may vary in relation to different experimental parameters, such as target probability and stimulus semantics. Thus, we proposed a novel method, Target Recognition using Image Complexity Priori (TRICP) algorithm, in which the image information is introduced in the calculation of the interest score in the RSVP paradigm. The method combines information from the image and EEG to enhance the accuracy of single-trial P300 detection on the basis of traditional single-trial P300 detection algorithm. We defined an image complexity parameter based on the features of the different layers of a convolution neural network (CNN). We used the TRICP algorithm to compute for the complexity of an image to quantify the effect of different complexity images on the P300 components and training specialty classifier according to the image complexity. We compared TRICP with the HDCA algorithm. Results show that TRICP is significantly higher than the HDCA algorithm (Wilcoxon Sign Rank Test, p

To address the challenge of unprecedented growth in mobile data traffic, ultra-dense network deployment is a cost efficient solution to offload the traffic over some small cells. The overlapped coverage areas of small cells create more than one candidate access points for one mobile station. Signal strength based user association in IEEE 802.11 results in a significantly unbalanced load distribution among access points. However, the bandwidth demand of each user actually differs vastly due to their different preferences on mobile applications. In this paper, we formulate a non-linear integer programming model for joint user association and user bandwidth demand guarantee problem. In this model, we try to maximize the system capacity and guarantee the effective bandwidth demand for each user by soft-association control. Finally, we evaluate the proposed algorithm performance for the edge users with dynamic and heterogeneous bandwidth demands. Simulation results show that the proposed soft-association control performs better than the distributed ones and improves the individual quality of user experience with a little price on system throughput.

With the aim to simplify network management and control, Software defined network is proposed as a new paradigm and architecture in networking areas. The concept of Cloud and cognitive cellular network will be important features in the next generation radio access networks. In this paper, we propose a distributed hierarchical architecture for heterogeneous radio access networks based on OpenFlow. SDN architecture enables resource and infrastructure sharing among heterogeneous radio access networks. Mobility management in this new heterogeneous radio access network architecture and a OpenFlow-enabled node architecture for AP infrastructures are defined. We compared it with the one defined in 3GPP LTE standard to show the required changes. An architecture of cognitive information processing is defined to support new features of mobility management, which is taken as an service of network operation system. In the end, typical network applications of mobility management in this SDN architecture are introduced. New topics with technical challenges are analyzed in this SDN based heterogeneous RANs towards the ongoing research and prototypes.