The current massive use of digital communications demands a secure link by using an embedded system（ES） with data encryption at the protocol level. The serial peripheral interface（SPI） protocol is commonly used by...The current massive use of digital communications demands a secure link by using an embedded system（ES） with data encryption at the protocol level. The serial peripheral interface（SPI） protocol is commonly used by manufacturers of ESs and integrated circuits for applications in areas such as wired and wireless communications. We present the design and experimental implementation of a chaotic encryption and decryption algorithm applied to the SPI communication protocol. The design of the chaotic encryption algorithm along with its counterpart in the decryption is based on the chaotic Hénon map and two methods for blur and permute（in combination with DNA sequences）. The SPI protocol is configured in 16 bits to synchronize a transmitter and a receiver considering a symmetric key. Results are experimentally proved using two low-cost dsPIC microcontrollers as ESs. The SPI digital-to-analog converter is used to process, acquire, and reconstruct confidential messages based on its properties for digital signal processing. Finally, security of the cryptogram is proved by a statistical test. The digital processing capacity of the algorithm is validated by dsPIC microcontrollers.展开更多
Previous efforts to boost the performance of brain-computer interfaces (BCIs) have predominantly focused on optimizing algorithms for decoding brain signals. However, the untapped potential of leveraging brain plastic...Previous efforts to boost the performance of brain-computer interfaces (BCIs) have predominantly focused on optimizing algorithms for decoding brain signals. However, the untapped potential of leveraging brain plasticity for optimization remains underexplored. In this study, we enhanced the temporal resolution of the human brain in discriminating visual stimuli by eliminating the attentional blink (AB) through color-salient cognitive training, and we confirmed that the mechanism was an attention-based improvement. Using the rapid serial visual presentation (RSVP)-based BCI, we evaluated the behavioral and electroencephalogram (EEG) decoding performance of subjects before and after cognitive training in high target percentage (with AB) and low target percentage (without AB) surveillance tasks, respectively. The results consistently demonstrated significant improvements in the trained subjects. Further analysis indicated that this improvement was attributed to the cognitively trained brain producing more discriminative EEG. Our work highlights the feasibility of cognitive training as a means of brain enhancement to boost BCI performance.展开更多
基金Acknowledgements： This work was partially supported by Natural Science Foundation of Liaoning Province, China （No. 20042042）, Specialized Research Fund for the Doctoral Program of Higher Education of China （No. 20030145017）.
基金Project supported by the CONACYT,México(No.166654)
文摘The current massive use of digital communications demands a secure link by using an embedded system（ES） with data encryption at the protocol level. The serial peripheral interface（SPI） protocol is commonly used by manufacturers of ESs and integrated circuits for applications in areas such as wired and wireless communications. We present the design and experimental implementation of a chaotic encryption and decryption algorithm applied to the SPI communication protocol. The design of the chaotic encryption algorithm along with its counterpart in the decryption is based on the chaotic Hénon map and two methods for blur and permute（in combination with DNA sequences）. The SPI protocol is configured in 16 bits to synchronize a transmitter and a receiver considering a symmetric key. Results are experimentally proved using two low-cost dsPIC microcontrollers as ESs. The SPI digital-to-analog converter is used to process, acquire, and reconstruct confidential messages based on its properties for digital signal processing. Finally, security of the cryptogram is proved by a statistical test. The digital processing capacity of the algorithm is validated by dsPIC microcontrollers.
基金supported by the National Natural Science Foundation of China (U19B2030, 61976167, 62301405, 62101416)the Natural Science Basic Research Program of Shaanxi, China (2022JQ-708)Fundamental Research Funds for the Central Universities, China.
文摘Previous efforts to boost the performance of brain-computer interfaces (BCIs) have predominantly focused on optimizing algorithms for decoding brain signals. However, the untapped potential of leveraging brain plasticity for optimization remains underexplored. In this study, we enhanced the temporal resolution of the human brain in discriminating visual stimuli by eliminating the attentional blink (AB) through color-salient cognitive training, and we confirmed that the mechanism was an attention-based improvement. Using the rapid serial visual presentation (RSVP)-based BCI, we evaluated the behavioral and electroencephalogram (EEG) decoding performance of subjects before and after cognitive training in high target percentage (with AB) and low target percentage (without AB) surveillance tasks, respectively. The results consistently demonstrated significant improvements in the trained subjects. Further analysis indicated that this improvement was attributed to the cognitively trained brain producing more discriminative EEG. Our work highlights the feasibility of cognitive training as a means of brain enhancement to boost BCI performance.
