To meet the demands of regional mapping with synthetic aperture radar (SAR) satellites, this study proposes a multistrip stitching imaging mission planning (MSIMP) method that considers energy consumption. Accounting for both regional coverage rate and satellite energy consumption, an MSIMP model was constructed. The model maximizes the region coverage benefit (RCB), minimizes the total imaging time (TIT) as the optimization objectives, and sets the indices of orbit selection, side-swing angle coefficients, and determination coefficients of the start time and end times of each orbit as the decision variables to realize the overall optimal scheduling of available orbital resources (ORs), side-swing angles, and imaging time of satellites. To address the problem of the large-scale and diverse types of decision variables in the MSIMP model, an improved particle swarm optimization (PSO) algorithm with a hybrid local search and differential evolution (LSDE) strategy (LSDE-PSO) that implements differential operations and local searches on two types of particles at various stages of the evolution process to enhance the model’s solution efficiency was proposed. The proposed method was validated using three simulation scenarios with different regional sizes. The experimental results showed that the proposed method demonstrated consistent adaptability to different-scale tasks, achieving the synchronized optimization of coverage revenue and on-orbit energy consumption. Compared with existing algorithms, the proposed LSDE-PSO algorithm can obtain the optimal imaging scheme with a higher RCB with lower TIT at almost the same computational cost, which provides significant technical support for mission planning for regional SAR satellite imaging in practical applications.

This paper demonstrates that thefractal dimension of frame preambleserves as a new radiometric feature that can be used together with other known radiometric features to enhance the identification accuracy in wireless device identification. We first propose a fractal dimension estimation scheme for direct-sequence spread spectrum (DSSS) frame preamble, then provide theoretical analysis to reveal how the fractal dimension is primarily determined by the device hardware imperfections, and thus prove that the fractal dimension serves as an intrinsic radiometric feature. We further show simulation results to verify our theoretical modeling of the fractal dimension and also numerically evaluate the effects of device hardware imperfections and wireless channels on the fractal dimension. Finally, by jointly applying the fractal dimension and the five features reported in the literature, we conduct extensive experiments to demonstrate that the fractal dimension can lead to a further improvement of the state-of-the-art result in the radiometric feature-based device identification.

Attention Deficit Hyperactivity Disorder (ADHD) is a high incidence of neurobehavioral disease in school-age children. Its neurobiological classification is meaningful for clinicians. The existing ADHD classification methods suffer from two problems, i.e., insufficient data and noise disturbance. Here, a high-accuracy classification method is proposed, which uses brain Functional Connectivity (FC) as material for ADHD feature analysis. In detail, we introduce a binary hypothesis testing framework as the classification outline to cope with insufficient data of ADHD database. Under binary hypotheses, the FCs of test data are allowed to use for training and thus affect the subspace learning of training data. To overcome noise disturbance, an l2,1-norm LDA model is adopted to robustly learn ADHD features in subspaces. The subspace energies of training data under binary hypotheses are then calculated, and an energy-based comparison is finally performed to identify ADHD individuals. On the platform of ADHD-200 database, the experiments show our method outperforms other state-of-the-art methods with the significant average accuracy of 97.6%.