FPGA implementation and image encryption application of a new PRNG based on a memristive Hopfield neural network with a special activation gradient

A memristive Hopfield neural network(MHNN)with a special activation gradient is proposed by adding a suitable memristor to the Hopfield neural network(HNN)with a special activation gradient.The MHNN is simulated and dynamically analyzed,and implemented on FPGA.Then,a new pseudo-random number generator(PRNG)based on MHNN is proposed.The post-processing unit of the PRNG is composed of nonlinear post-processor and XOR calculator,which effectively ensures the randomness of PRNG.The experiments in this paper comply with the IEEE 754-1985 high precision32-bit floating point standard and are done on the Vivado design tool using a Xilinx XC7 Z020 CLG400-2 FPGA chip and the Verilog-HDL hardware programming language.The random sequence generated by the PRNG proposed in this paper has passed the NIST SP800-22 test suite and security analysis,proving its randomness and high performance.Finally,an image encryption system based on PRNG is proposed and implemented on FPGA,which proves the value of the image encryption system in the field of data encryption connected to the Internet of Things(Io T).     

硝酸辅助合成水溶性绿光碳点及其在pH测量与Fe3+检测中的应用

以柠檬酸、甲酰胺与浓硝酸为原料合成了绿色荧光碳点（CDs）,对其物相、形貌及荧光性能进行了表征,并研究了其在离子检测及荧光标记方面的应用。结果表明：所合成的CDs呈现良好的水溶性;当以360~460 nm波长的光激发时,该CDs发出明亮的绿色荧光,并且发射峰的位置不随激发光波长而改变,其量子产率高达44.2%;同时,该绿光CDs对pH敏感,体系pH值在4.5~8.5范围内时,其归一化荧光强度与pH间有较好的线性关系;此外,该绿光CDs对Fe³⁺有较好的选择性响应,在0~1 000 μmol·L^-1范围内,体系的荧光淬灭效率（I₀/I）与Fe³⁺浓度有良好的线性关系,检出限为9.8 μmol·L^-1。