Traditional video encryption methods protect video content by encrypting each frame individually. However, in resource-constrained environments, this approach consumes significant computational resources. To overcome this challenge, this paper proposes a novel method called “Encrypt a story (EAS)”, which aims to enhance encryption efficiency by focusing on encrypting specific segments of the video rather than encrypting each frame. The EAS refers to selecting segments in the time dimension of the video that contain important information or key events for encryption. This method leverages video segmentation techniques to focus encryption efforts on continuous key frames, significantly reducing the consumption of computational resources. To address the need for a large number of key streams during the encryption process, this paper proposes a discrete sinusoidal memristive Rulkov neuron map (DSM-RNM). Through attractor analysis, complexity comparison, Lyapunov exponent, and NIST tests, we validated its ability to generate high-performance pseudorandom sequences, which significantly enhances the security of the encryption algorithm. Notably, the DSM-RNM is shown to exhibit a phenomenon of infinitely coexisting attractors. Furthermore, by constructing a digital circuit to capture the attractors of the DSM-RNM, its potential for industrial applications is demonstrated. Evaluation results show that the EAS saves approximately 90% of the time while ensuring security, exhibiting strong practicality and efficiency

The resistance state of a memristor can be influenced by external stimuli, and these variations can be converted into a pseudorandom sequence through appropriate circuitry and control mechanisms. By leveraging this property, a reliable and complex pseudorandom number generator suitable for encryption can be designed. To enhance the chaotic complexity of memristor-based discrete systems, this paper introduces a three-dimensional hyperchaotic map based on a memristor (3D-HMBM), which integrates a sine-function nonlinearity with a discrete memristor model. Analyzing its dynamical properties via Lyapunov exponents, the 3D-HMBM exhibits evolution from periodicity to chaos and hyperchaos. The complexity of its iterated sequences is verified through metrics such as Spectral Entropy and C0 complexity. Furthermore, the 3D-HMBM displays a unique phenomenon of infinite coexisting attractors. As initial values vary, the system generates attractors at different positions, suggesting that—in theory—an infinite number of attractors exist. Finally, the simulation results are validated via digital-circuit implementation. Building on this foundation, we propose a multi-image encryption algorithm based on the 3D-HMBM, offering a more secure solution for encrypting large volumes of data. Through statistical testing and cryptographic analysis, we confirm the significant potential of the keystream generated by the 3D-HMBM for cryptographic applications.

With the in-depth exploration of the ocean, the Internet of Underwater Things (IoUT) technology has attracted increasing attention. Underwater wireless optical communication (UWOC) provides the possibility of massive data transmission for IoUT. The underwater optical link of UWOC can be interrupted by the obstruction of marine animals and plants, seamounts, and some underwater equipment. In this paper, we present a reconfigurable intelligent surface (RIS) assisted UWOC system to solve the problem of link occlusion. The cascaded turbulence channel fading coefficients from source to destination through RIS are modeled as Gamma-Gamma distribution, and the pointing errors caused by beam jitter and RIS jitter are considered. In the RIS-assisted UWOC systems with intensity modulation and direct detection (IM-DD), we apply the approximate distribution of the sum of Gamma-Gamma random variables to derive the probability density function (PDF) of the instantaneous received signal-to-noise ratio (SNR) for the first time. Based on the PDF, the novel closed-form expression of outage probability is given, and the outage probability analysis results show that when the number of reflecting elements is very large (i.e., tends to infinity), the outage probability asymptotically approaches zero. Subsequently, the closed-form expressions of the finite-SNR diversity order, the asymptotical diversity order, and the convergence speed of finite-SNR diversity order to asymptotical diversity order are proposed. Additionally, the ergodic channel capacity expression is investigated. Finally, we give simulation results to validate our derived results and analyze the impacts of the number of reflecting elements of RIS and the pointing errors on the above system performance indicators.

The new generation of artificial intelligence (AI) is evolving from perception-oriented processing toward cognition-oriented intelligence, emphasizing learning, memory, decision-making, and adaptive behavior inspired by the human brain. In this paper, a memristor-based perceptual decision-making neural network circuit (PDNNC) is proposed to integrate multiple classical conditioning mechanisms and fear generalization within a unified hardware framework. Unlike existing studies that focus on isolated learning behaviors, the proposed circuit architecture simultaneously realizes associative learning, forgetting, latent inhibition, blocking effects, secondary conditioning, and emotion-modulated decision-making at the circuit level. A voltage-controlled threshold-type memristor model is employed to emulate synaptic plasticity, while modular perceptual and decision-making subcircuits are designed to ensure scalability and functional consistency. The complete circuit implementation is verified using PSPICE circuit simulations. Simulation results demonstrate the correct realization of diverse classical conditioning behaviors, controllable fear generalization, and adaptive decision responses under different perceptual stimuli. The proposed bio-inspired circuit has vast potential applications in realizing cognitive intelligence and robotics.

This paper investigates optical orthogonal frequency division multiplexing (O-OFDM) systems with index modulation (IM) based on discrete Hartley transform (DHT) for visible light communications (VLC). An interesting trade-off between spectral efficiency (SE), energy efficiency, peak-to-average power ratio (PAPR) and bit error rate (BER) performance can be made by using the IM technique. And the DHT-based O-OFDM-IM systems can achieve higher SE compared with the DFT (discrete Fourier transform)-based counterparts. Employing DHT instead of DFT enables the removal of the Hermitian symmetry requirement, which contributes to transmitting more index bits and results in SE improvement. To acquire the same SE, the proposed systems can significantly lower the constellation order leading to better BER performance by contrast with the DFT-based counterparts. We employ DC (direct current) biasing and AC (Asymmetrically clipped) techniques to investigate the O-OFDM-IM systems in this paper. Simulation results indicate that SNR gains can be obtained by the DHT-based DCO/ACO-IM in comparison to the DFT-based counterparts. We also propose a novel reduced-complexity ML (maximum likelihood) detector specifically applicable to VLC. The new detector exhibits the same BER performance as the ML detector and performs better than the LLR (log-likelihood ratio) detector with reduced computational complexity.

With the fast growth of information technology (IT), the safety of image transmission and the storing of images are becoming increasingly important. Traditional image encryption algorithms have certain limitations in transmission and security, so there is an urgent need for a secure and reliable image encryption algorithm. A universal compression sensing (CS) image encryption (IE) algorithm based on DNA-triploid mutation (DTM) is presented in this paper. Firstly, by using the CS algorithm, an image is compressed while obtaining a range of chaotic sequences by iteration of a chaotic map. Then, DNA sequences are generated by encoding the image and, based on the DTM, new mutant DNA sequences are generated according to specific rules. Next, the chaotic sequences are operated at the DNA level to perform confusion and diffusion operations on the image to ensure the security of the data. Finally, DNA decoding is carried out to obtain the compressed encrypted image. The simulation results show that the algorithm can effectively complete encryption and decryption of images. The performance test results show that the algorithm has a sufficiently large key space of 10⁵⁸⁷. The information entropy of the cipher image is close to 8. In summary, both simulation experiments and performance tests fully show that a high level of security and reliability for the proposed algorithm in protecting image privacy is achieved.