CNN intelligent early warning for apple skin lesion image acquired by infrared video sensors

Video sensors and agricultural IoT ( internet of things) have been widely used in the informa-tionalized orchards.In order to realize intelligent-unattended early warning for disease-pest, this pa-per presents convolutional neural network ( CNN) early warning for apple skin lesion image, which is real-time acquired by infrared video sensor.More specifically, as to skin lesion image, a suite of processing methods is devised to simulate the disturbance of variable orientation and light condition which occurs in orchards.It designs a method to recognize apple pathologic images based on CNN, and formulates a self-adaptive momentum rule to update CNN parameters.For example, a series of experiments are carried out on the recognition of fruit lesion image of apple trees for early warning. The results demonstrate that compared with the shallow learning algorithms and other involved, well-known deep learning methods, the recognition accuracy of the proposal is up to 96.08%, with a fairly quick convergence, and it also presents satisfying smoothness and stableness after conver-gence.In addition, statistics on different benchmark datasets prove that it is fairly effective to other image patterns concerned.     

加速RSA算法的定时攻击研究

为了研究以适应弱计算环境提高密码运算速度为目标而经过Montgomery和中国剩余定理等方法加速后RSA算法的密码时间相关安全性,论文分析了Montgomery模幂运算中额外约简的发生几率,输入参数与运行时间之间的关系,提出了改进的定时按住比特搜索攻击方法,选择密文输入,对比运算耗时差量定时,按位搜索RSA的秘密因子,并在深腾1800高性能集群平台上进行了有关实验.研究显示:改进后的RSA算法虽能较好地免疫于传统定时攻击,但是按住比特搜索方法理论上完全能够搜索出RSA的素因子秘密参量.