In this article, the model-free robust formation control problem is addressed for cooperative underactuated quadrotors involving unknown nonlinear dynamics and disturbances. Based on the hierarchical control scheme and the reinforcement learning theory, a robust controller is proposed without knowledge of each quadrotor dynamics, consisting of a distributed observer to estimate the position state of the leader, a position controller to achieve the desired formation, and an attitude controller to control the rotational motion. Simulation results on the multiquadrotor system confirm the effectiveness of the proposed model-free robust formation control method.Recent research achievements in learning from demonstration (LfD) illustrate that the reinforcement learning is effective for the robots to improve their movement skills. The current challenge mainly remains in how to generate new robot motions automatically to perform new tasks, which have a similar preassigned performance indicator but are different from the demonstration tasks. To deal with the abovementioned issue, this article proposes a framework to represent the policy and conduct imitation learning and optimization for robot intelligent trajectory planning, based on the improved locally weighted regression (iLWR) and policy improvement with path integral by dual perturbation (PI²-DP). Besides, the reward-guided weight searching and basis function's adaptive evolving are performed alternately in two spaces, i.e., the basis function space and the weight space, to deal with the abovementioned problem. The alternate learning process constructs a sequence of two-tuples that join the demonstration task and new one together for motor skill transfer, so that the robot gradually acquires motor skill, from the task similar to demonstration to dissimilar tasks with different performance metrics. Classical via-points trajectory planning experiments are performed with the SCARA manipulator, a 10-degree of freedom (DOF) planar, and the UR robot. These results show that the proposed method is not only feasible but also effective.Image compression has always been an important topic in the last decades due to the explosive increase of images. The popular image compression formats are based on different transforms which convert images from the spatial domain into compact frequency domain to remove the spatial correlation. In this paper, we focus on the exploration of data-driven transform, Karhunen-Loéve transform (KLT), the kernels of which are derived from specific images via Principal Component Analysis (PCA), and design a high efficient KLT based image compression algorithm with variable transform sizes.  https://www.selleckchem.com/products/ipi-549.html  To explore the optimal compression performance, the multiple transform sizes and categories are utilized and determined adaptively according to their rate-distortion (RD) costs. Moreover, comprehensive analyses on the transform coefficients are provided and a band-adaptive quantization scheme is proposed based on the coefficient RD performance. Extensive experiments are performed on several class-specific images as well as general images, and the proposed method achieves significant coding gain over the popular image compression standards including JPEG, JPEG 2000, and the state-of-the-art dictionary learning based methods.Recently, many new applications arose for multispectral and hyper-spectral imaging. Besides modern biometric systems for identity verification, also agricultural and medical applications came up, which measure the health condition of plants and humans. Despite the growing demand, the acquisition of multi-spectral data is up to the present complicated. Often, expensive, inflexible, or low resolution acquisition setups are only obtainable for specific professional applications. To overcome these limitations, a novel camera array for multi-spectral imaging is presented in this article for generating consistent multispectral videos. As differing spectral images are acquired at various viewpoints, a geometrically constrained multi-camera sensor layout is introduced, which enables the formulation of novel registration and reconstruction algorithms to globally set up robust models. On average, the novel acquisition approach achieves a gain of 2.5 dB PSNR compared to recently published multi-spectral filter array imaging systems. At the same time, the proposed acquisition system ensures not only a superior spatial, but also a high spectral, and temporal resolution, while filters are flexibly exchangeable by the user depending on the application. Moreover, depth information is generated, so that 3D imaging applications, e.g., for augmented or virtual reality, become possible. The proposed camera array for multi-spectral imaging can be set up using off-the-shelf hardware, which allows for a compact design and employment in, e.g., mobile devices or drones, while being cost-effective.Dictionary learning plays a significant role in the field of machine learning. Existing works mainly focus on learning dictionary from a single domain. In this paper, we propose a novel projective double reconstructions (PDR) based dictionary learning algorithm for cross-domain recognition. Owing the distribution discrepancy between different domains, the label information is hard utilized for improving discriminability of dictionary fully. Thus, we propose a more flexible label consistent term and associate it with each dictionary item, which makes the reconstruction coefficients have more discriminability as much as possible. Due to the intrinsic correlation between cross-domain data, the data should be reconstructed with each other. Based on this consideration, we further propose a projective double reconstructions scheme to guarantee that the learned dictionary has the abilities of data itself reconstruction and data crossreconstruction. This also guarantees that the data from different domains can be boosted mutually for obtaining a good data alignment, making the learned dictionary have more transferability. We integrate the double reconstructions, label consistency constraint and classifier learning into a unified objective and its solution can be obtained by proposed optimization algorithm that is more efficient than the conventional l1 optimization based dictionary learning methods. The experiments show that the proposed PDR not only greatly reduces the time complexity for both training and testing, but also outperforms over the stateof- the-art methods.