Wireless body area network (WBAN) is a crucial tool in modern medical areas. This refers to the intelligent interconnection of wireless sensor nodes installed outside or inside the human body to recover some human vital signs. The security and bandwidth saturation remain crucial problems of this technology. In this work, a cross-ring-based neural network model (CRNN) is obtained from the Hopfield neural network definition and its complex dynamics are deeply analyzed. The steady-state study shows that the model has a unique equilibrium point and its analysis shows that the model’s dynamics is self-excited. Based on the two-parameter Lyapunov spectrum, the set of synaptic weights has been quickly identified to illustrate hyperchaotic behavior of the CRNN. With the help of bifurcation plots, graphs of the Lyapunov spectrum, and phase portraits, we have characterized periodic, chaotic, and hyperchaotic patterns in the model. Furthermore, an experimental setup has been built using microcontroller technology to further support the results of the numerical simulations. A parallel compressive sensing algorithm combined with a nonlinear congruent generator has been used to show the application of the newly designed CRNN to medical image compression and security. Security and compression performances indicate an efficient scheme with the capability to resist various attacks and the capability to produce low-size data image from large-size data image. For instant from$256\times 256 $image size, an encrypted and compressed output image is obtained at the compression rate of 0.5, processing time of 0.147 ms, encryption throughput of 1783.3 MBits/s, for a 2.9-GHz processor, the number of cycles to process the algorithm is 1.62. Consequently, the proposal can be applied to WBANs.

The complex dynamics of a simple memristive tabu learning neuron (MTLN) are considered in this article. The analysis of the stability of its equilibria revealed that it displays self-excited dynamics. The investigation of the dynamics of the considered model highlighted that it is extremely sensitive to the initial conditions. That sensitivity to the initial conditions is supported by the coexistence of an infinite number of stable states for the same set of system parameters but using different initial states. Among the infinity of coexisting stable states, there are periodic, quasiperiodic, and chaotic ones. The coexistence of an infinite number of chaotic attractors found in this work and not yet reported in such a model represents the first important contribution of this work. The circuit of the coupled neuron is also realized in the PSPICE simulation environment to further support the obtained result in extreme multistability. Therefore, the revealed chaotic dynamics of the MTLN is applied to compress and encrypt digital medical images. The compressed sensing (CS) approach is combined with deoxyribonucleic acid (DNA) coding/decoding to achieve high compression/encryption performances, including very low computational cost (encryption timet= 0.162 ms, encryption throughput ET = 1618.1 MB/s, number of CPU cycles NC = 1.8) useful for real-time compression. The compression/encryption method developed in this work represents the second main contribution based on the results of the analysis metrics.