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.

Fast DRAM increasingly dominates infrastructure spend in large scale computing environments and this trend will likely worsen without an architectural shift. The cost of deployed memory can be reduced by replacing part of the conventional DRAM with lower cost albeit slower memory media, thus creating a tiered memory system where both tiers are directly addressable and cached. But, this poses numerous challenges in a highly multi-tenant warehouse-scale computing setting. The diversity and scale of its applications motivates an application-transparent solution in the general case, adaptable to specific workload demands.

Dictionary learning has been widely used in image representation under the framework of sparse theory. But most of the current dictionary learning strategies can only be used for single-frame image separately, which are insufficient from the perspective of incremental information acquisition and global optimization for the sequential or multiframe satellite images. To this end, this paper proposes an incremental dictionary learning method for multiframe satellite images representation in the spectral domain. The incremental dictionary learning is formulated analytically in the framework of sparse representation with low-rank constraint, as a frame-by-frame gradual optimization process of global and local dictionaries, and their corresponding sparse coefficients with the sequence. Specifically, the global dictionary representing the common spectral information of the sequential frames, is optimized by two adjacent frames gradually. Meanwhile, the local dictionary representing the specific spectral information of each frame, is optimized by the newly added frame itself. In addition, an activity ratio for separating the global dictionary from the local dictionaries, an outlier detection method for initializing the local dictionary are also given, and the alternating direction methods of multipliers (ADMM) is employed to implement the above optimization. Comparison results with the related state-of-the-art methods on different datasets demonstrate that, our proposed method achieves the best representation performance in both spatial and spectral domains, and also helps to improve the performance of dictionary-based tasks using sequential satellite images, such as sea surface anomaly detection.

The performance of deep learning heavily depend on the quantity and quality of training data. But in many fields, well-annotated data are so difficult to collect, which makes the data scale hard to meet the needs of network training. To deal with this issue, a novel data augmentation method using the bitplane information recombination model (termed as BIRD) is proposed in this paper. Considering each bitplane can provide different structural information at different levels of detail, this method divides the internal hierarchical structure of a given image into different bitplanes, and reorganizes them by bitplane extraction, bitplane selection and bitplane recombination, to form an augmented data with different image details. This method can generate up to 62 times of the training data, for a given 8-bits image. In addition, this generalized method is model free, parameter free and easy to combine with various neural networks, without changing the original annotated data. Taking the task of target detection for remotely sensed images and classification for natural images as an example, experimental results on DOTA dataset and CIFAR-100 dataset demonstrated that, our proposed method is not only effective for data augmentation, but also helpful to improve the accuracy of target detection and image classification.