The paper studies a novel method for real-time solutions of the two-player pursuit-evasion game. The min-max principle is adopted to confirm the Nash equilibrium of the game. As agents in the game can form an Internet of Things (IoT) system, the real-time control law of each agent is obtained by taking a linear-quadratic cost function in adaptive dynamic programming. By introducing the Lyapunov function, we consider the scenario when capture occurs. Since most actual systems are continuous, the policy iteration algorithm is used to make the real-time policy converge to the analytical solution of the Nash equilibrium. Furthermore, we employ the value function approximation method to calculate the neural network parameters without directly solving the Hamilton–Jacobi–Isaacs equation. Simulation results depict the method’s feasibility in different scenarios of the pursuit-evasion game.

In outdoor environments or environments with space restrictions, it is difficult to transport and use conventional computed tomography (CT) systems. Therefore, there is an urgent need for rapid reconstruction of portable cone-beam CT (CBCT) systems. However, owing to its portability and the characteristics of temporary construction environments, high precision spatial location is difficult to achieve with portable CBCT systems. To overcome these limitations, we propose an iterative self-calibration improvement method with a self-calculated initial value based on the projection relationship and image features. The CT value of an open field image was used as the weight value of the projection data in the subsequent experiments to reduce the nonlinear attenuation of the projection intensity. Subsequently, an initial value was obtained based on the invariance of the rotation axis. Finally, self-calibration was realized iteratively using the reconstructed image. This method overcomes the main problem of the rotation axis invariance calibration algorithm—high similarity between the adjacent positions of symmetrical homogeneous materials. The proposed method not only improves the precision of self-calibration based on the projection relationship, but also reduces the performance cost and solution time of the self-calibration algorithm based on the image features. Thus, it satisfies the precision requirements for self-calibration of portable CBCT systems.

X-ray tomography is often affected by noise and artifacts during the reconstruction process, such as detector offset, calibration errors, metal artifacts, etc. Conventional algorithms, including FDK and SART, are unable to satisfy the sampling theorem requirements for 3D reconstruction under sparse-view constraints, exacerbating the impact of noise and artifacts. This paper proposes a novel 3D reconstruction algorithm tailored to sparse-view cone-beam computed tomography (CBCT). Drawing upon compressed sensing theory, we incorporate the weighted Schatten p-norm minimization (WSNM) algorithm for 2D image denoising and the adaptive steepest descent projection onto convex sets (ASD-POCS) algorithm, which employs a total variation (TV) regularization term. These inclusions serve to reduce noise and ameliorate artifacts. Our proposed algorithm extends the WSNM approach into three-dimensional space and integrates the ASD-POCS algorithm, enabling 3D reconstruction with digital brain phantoms, clinical medical data, and real projections from our portable CBCT system. The performance of our algorithm surpasses traditional methods when evaluated using root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) metrics. Furthermore, our approach demonstrates marked enhancements in artifact reduction and noise suppression.