This work proposes a reliable control scheme to attain the precise tracking control of multirotor unmanned aerial vehicle systems used for smart agriculture. The nonlinear mathematical model of a co-axial octorotor system equipped with a spraying mechanism is first established to contain the time-varying inertial coefficients and varying payload effects. Then an adaptive backstepping controller scheme is proposed to attain the desired attitude and position tracking. To ensure robustness against parameter uncertainty and external disturbances, a high-order integral sliding mode controller is integrated with the adaptive backstepping controller. Numerical simulations are carried out in variable payload conditions to demonstrate the effectiveness of the proposed approach.

As the number and proportion of compute-intensive tasks on mobile devices increase, so does the amount of energy required to process them. Edge computing technologies provide a solution to enhance the capacity of mobile devices by offloading those tasks to edge servers. Based on dynamic voltage and frequency scaling (DVFS) technology, this paper proposed a new energy saving offloading strategy for mobile devices. On the premise that the task completion time meets the task deadline, the energy consumption problem of mobile devices is formulated as a minimum problem and solved by the genetic algorithm. In this strategy, priority is allocated to each task of workflow, and corresponding offloading resources are allocated according to the result of classification. DVFS technology is applied to the CPU of the terminal device to further reduce energy consumption while the task-resource mapping is performed. Experimental results show that for the same workflow and the same offloading resources and communication environment, in meet the delay constraints of tasks, the proposed algorithm is compared with the existing fine-grained task migration energy-saving algorithm based on genetic algorithm and the task scheduling algorithm based on energy consumption perception, can effectively improve the high energy consumption of mobile devices.

The emergence of edge computing has greatly improved the computing and storage capabilities of mobile terminals and IoT devices. However, most of the current research in the edge computing field focuses on computing offloading, resource allocation, and computing migration, and there is very little research on the placement of edge servers. To address the problem of edge server placement, we have proposed an edge server placement algorithm (MRT) based on the suitability evaluation of access points in the 5G ultra-dense network environment. The algorithm first uses the analytic hierarchy process (AHP) and the entropy weight method to evaluate the suitability of each access point based on characteristic indicators to determine whether the access point is suitable for placing edge servers. Then, according to the suitability evaluation results, the solution space of the edge server placement is reduced, and the optimal solution for the edge server is found in the reduced solution space through the tabu search method. Sufficient experimental results show that the proposed MRT algorithm can effectively reduce the average response delay of computing tasks, and the algorithm performance is better than other typical placement algorithms.

SUMMARY: Rare copy number variations (CNVs) are frequent causes of genetic diseases. We developed a graphical software package based on a novel approach that can consistently identify CNVs of all types (homozygous deletions, heterozygous deletions, heterozygous duplications) from exome-sequencing data without the need of a paired control. The algorithm compares coverage depth in a test sample against a background distribution of control samples and uses principal component analysis to remove batch effects. It is user friendly and can be run on a personal computer. AVAILABILITY AND IMPLEMENTATION: The main scripts are implemented in R (2.15), and the GUI is created using Java 1.6. It can be run on all major operating systems. A non-GUI version for pipeline implementation is also available. The program is freely available online: https://sourceforge.net/projects/fishingcnv/ SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.