Scattering correction method for panel detector based cone beam computed tomography system

A scattering correction method for a panel detector based cone beam computed tomography system is presented. First, the x-ray spectrum of the system is acquired by using the Monte Carlo simulation method. Secondly, scattered photon distribution is calculated and stored as correction matrixes by using the Monte Carlo simulation method according to scanned objects and computed tomography system specialties. Thirdly, scattered photons are removed from projection data by correction matrixes. A comparison of reconstruction image between before and after scattering correction demonstrates that the scattering correction method is effective for the panel detector based cone beam computed tomography system.

Cryptanalysis and Improvement of a Chaotic Map-Control-Based and the Plain Image-Related Cryptosystem

Due to the characteristics of chaotic systems,different cryptosystems based on chaos have been proposed to satisfy the security of multimedia data.A plain image-related chaotic algorithm is proposed by Luo et al.with high speed and efficiency.Security weaknesses of the cryptosystem are studied in this paper.It is found that the important secret key information is leaked because an important parameter can be obtained after an inverse operation in the last step of the cryptosystems without secret key.Meanwhile,the value zero is processed improperly in quantification algorithm.Based on the weaknesses,chosen plaintext attack on the cryptosystem is proposed,by which,an important parameter,equivalent to secret key,can be calculated with a specific chosen plain image.With the obtained parameter,the plain image of any ciphered image,encrypted by the cryptosystem,can be recovered.Then,an improvement is proposed to solve the problems after modifying the quantification algorithm.It is from the experiments that chosen plaintext attack is valid and improved algorithm possesses better performance.

Antenna Selection in Energy Harvesting Relaying Networks Using Q-Learning Algorithm

In this paper,a novel opportunistic scheduling(OS)scheme with antenna selection(AS)for the energy harvesting(EH)cooperative communication system where the relay can harvest energy from the source transmission is proposed.In this considered scheme,we take into both traditional mathematical analysis and reinforcement learning(RL)scenarios with the power splitting(PS)factor constraint.For the case of traditional mathematical analysis of a fixed-PS factor,we derive an exact closed-form expressions for the ergodic capacity and outage probability in general signal-to-noise ratio(SNR)regime.Then,we combine the optimal PS factor with performance metrics to achieve the optimal transmission performance.Subsequently,based on the optimized PS factor,a RL technique called as Q-learning(QL)algorithm is proposed to derive the optimal antenna selection strategy.To highlight the performance advantage of the proposed QL with training the received SNR at the destination,we also examine the scenario of QL scheme with training channel between the relay and the destination.The results illustrate that,the optimized scheme is always superior to the fixed-PS factor scheme.In addition,a better system parameter setting with QL significantly outperforms the traditional mathematical analysis scheme.