No abstract

As an integral part of reliable communication in wireless networks, effective link estimation is essential for routing protocols. However, due to the dynamic nature of wireless channels, accurate link quality estimation remains a challenging task. In this article, we propose 4C, a novel link estimator that applies link quality prediction along with link estimation. Our approach is data driven and consists of three steps: data collection, offline modeling, and online prediction. The data collection step involves gathering link quality data, and based on our analysis of the data, we propose a set of guidelines for the amount of data to be collected in our experimental scenarios. The modeling step includes offline prediction model training and selection. We present three prediction models that utilize different machine learning methods, namely, naive Bayes classifier, logistic regression, and artificial neural networks. Our models take a combination of PRR and the physical-layer information, that is, Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), and Link Quality Indicator (LQI) as input, and output the success probability of delivering the next packet. From our analysis and experiments, we find that logistic regression works well among the three models with small computational cost. Finally, the third step involves the implementation of 4C, a receiver-initiated online link quality prediction module that computes the short temporal link quality. We conducted extensive experiments in the Motelab and our local indoor testbeds, as well as an outdoor deployment. Our results with single- and multiple-senders experiments show that with 4C, CTP improves the average cost of delivering a packet by 20% to 30%. In some cases, the improvement is larger than 45%.

This work aims to analyze the relationship between root growth, mitogen-activated protein kinase (MAPK), auxin signaling, and cell cycle-related gene expression in cadmium (Cd)-stressed rice. The role of MAPKs in auxin signal modification and cell cycle-related gene expression during root growth was investigated by disrupting MAPK signaling using the MAPKK inhibitor PD98059 (PD). Treatment with Cd caused a significant accumulation of Cd in the roots. A Cd-specific probe showed that Cd is mainly localized in the meristematic zone and vascular tissues. Perturbation of MAPK signaling using PD significantly suppressed root system growth under Cd stress. The transcription of six MAPK genes was inhibited by Cd compared to the control. Detection using DR5-GUS transgenic rice showed that the intensity and distribution pattern of GUS staining was similar in roots treated with PD or Cd, whereas in Cd plus PD-treated roots, the GUS staining pattern was similar to that of the control, which indicates a close association of MAPK signaling with auxin homeostasis under control and Cd stress conditions. The expression of most key genes of auxin signaling, including OsYUCCA, OsPIN, OsARF, and OsIAA, and of most cell cycle-related genes, was negatively regulated by MAPKs under Cd stress. These results suggest that the MAPK pathway plays specific roles in auxin signal transduction and in the control of the cell cycle in response to Cd stress. Altogether, MAPKs take part in the regulation of root growth via auxin signal variation and the modified expression of cell cycle-related genes in Cd-stressed rice. A working model for the function of MAPKs in rice root systems grown under Cd stress is proposed.

In order to implement an unobstructed assessment of three-dimensional (3-D) gait, we developed a mobile force plate and 3-D motion analysis system (M3D) to measure triaxial ground reaction forces (GRF) and 3-D orientations of feet. Calibration and test experiments were conducted to characterize the sensor developed. To test the accuracy of the new measurement system, validation experiments by using the reference measurements of a commercially available measurement system were performed in a gait laboratory, where a stationary force plate, a motion capture system based on high-speed cameras and a motion track system of XSENS were adopted to analyze human movements. Experimental results supported the proposal that the developed system can be used to measure triaxial GRF and orientations with an acceptable precision during successive walking gait.