Template matching based on zero-mean normalized cross-correlation measure (ZNCC) has been widely used in a broad range of image processing applications. To meet the requirements for high processing speed, small size, and variable image size in automatic target recognition systems, a novel field-programmable gate array (FPGA)-based parallel architecture is presented in this paper for the ZNCC computation. The proposed architecture employs two groups of RAM blocks, one of which is used for the multiply-accumulate operations of the real and the reference images and the other for data rearrangement of the reference image, and their functions are switched through 2-input multiplexers when searching at the next row. Moreover, the sum of the pixels in the searching area of the real image is computed through serially accumulating the differences between the new column in the current searching area and the old column in the last searching area using one dual-port RAM. Simultaneously, the sum of the squares of the pixels is calculated in the same way. Using the Altera Stratix II FPGA chip (EP2S90F780I4) as the target device, the compilation results with Quartus II show that compared with the traditional architecture, the synthesis logic utilization decreases from 63% to 35% and the usage of DSP blocks decreases from 59% to 39%, while the memory bits only increase by 8% and the usage of other resources is nearly the same. The simulation and practical experimental results show that the proposed architecture can effectively improve the performance of the practical automatic target recognition system.

The 12-tetrahedral robot is an addressable reconfigurable technology (ART)-based variable geometry truss mechanism with 26 extensible struts and nine nodes arranged in a tetrahedral mesh. The robot has the capability of configuring its shape to adapt to environmental requirements, which makes it suitable for space exploration. This paper considers the motion planning problem for the robot in terms of gait planning and trajectory planning. First, a gait planning method is developed that limits the forward falling angles to only 25 degrees. Then, according to the given gait, the jerk-bounded method and inverse kinematics are utilized to calculate the trajectories of the nodes and the struts, respectively. A robot system model was built in ADAMS and simulations were conducted to demonstrate the feasibility of the motion planning method.

The paper puts forward and solves the problem of calculating the symmetric brother nodes of a given node in a perfect binary tree. By analyzing the relationships between a node and its ancestors, the position at which a node lies in term of the subtree rooted by an ancestor is expressed with a mathematical formula and consequently the mathematical formula to express its symmetric nodes is derived out. The formulas enable an easy calculation of a node and its symmetric nodes in the perfect binary tree.

This paper introduces an innovative decentralized adaptive tracking control strategy designed for interconnected systems by input saturation and unknown control coefficient. Broad learning system (BLS), a novel nonlinear approximation technique, is utilized for the identification of the unknown function. Leveraging the properties of Gaussian function, the auxiliary dynamic system is introduced to compensate for the input saturation effect. The Nussbaum-type function is introduced to counteract the difficulty produced by unknown control coefficient existed in the considered interconnected systems. On this basis, the decentralized control scheme is presented for the interconnected nonlinear systems utilizing both of adaptive control and dynamic surface control (DSC). To address the challenge of the “explosion of complexity”, a linear filter, accompanied by a compensation signal, is introduced. According to Lyapunov theory, all signals within the closed-loop systems remain bounded and the tracking errors exhibit convergence to the compact set near the origin. Ultimately, simulation studies are conducted to demonstrate the practicality of the approach.

A multi-strategy improved particle swarm optimization algorithm (MIPSO) based planning method is proposed for the time-optimal trajectory planning problem in the motion process of an aerial manipulator. Firstly, an aerial manipulator model is established and kinematic analysis is carried out. Secondly, 4-5-4 polynomial interpolation combined with MIPSO is fitted to the manipulator motion process to obtain the time-optimal variation curve of each joint. MIPSO non-linearly adjusts the inertia weights on the velocity formula, dynamically adjusts the learning factor, and adds the Cauchy variation operator when the position formula is updated. Besides, the algorithm introduces early convergence and automatic exit the iterative mechanism in itself. Finally, some simulation results show that the proposed algorithm can effectively improve the convergence speed of iteration, facilitate jumping out of the local optimal solution and ensure the smooth motion of the manipulator's trajectory without sudden changes.