In Internet of Things (IoT) scenarios, the limited computing resources and energy constraints of devices, alongside the growing demand for real-time applications, make secure image transmission challenging. To address this issue, we propose a lightweight image encryption scheme based on chaotic map. Firstly, a new 3-D inverse proportional chaotic map (3D-IPCM) is designed with good robustness. Dynamics confirms that it possesses key characteristics, including a broad and continuous chaotic range, all positive Lyapunov exponents (LEs), high permutation entropy (PE) complexity and even distribution. Then, a new pseudorandom number generator (PRNG) is designed based on this map, which successfully passes all NIST and TestU01 tests, even with 16-bit calculation precision. Next, this PRNG is employed for image encryption in IoT scenarios. In the cryptosystem, a chromosome crossover (CC)-based scrambling algorithm is proposed, along with a diffusion algorithm that achieves strong resistance to differential attack in just two rounds of row diffusion. Simulation and analysis verify that the cryptosystem has strong resistance to common attacks and low cost. For$256 \times 256$images, its average number of pixels change rate (NPCR) and unified average changing intensity (UACI) are 100% and 33.40%, respectively, and the encryption time is only 7.4 ms. Ultimately, we implement the algorithm on FPGA, thus confirming its capacity for parallel acceleration.

A universal modular plus parallel (MPP) method is proposed to construct enhanced chaotic maps, including one-dimensional MPP chaotic map (1D-MPPCM) and high-dimensional MPPCM (HD-MPPCM). It is theoretically proved that 1D-MPPCM model can significantly increase the Lyapunov exponent (LE) and parameter range of seed chaotic maps. To further increase the system dimension, the HD-MPPCM model is established through the close-loop parallel coupling mechanism. Based on several typical seed chaotic maps, some new parallel chaotic maps are obtained by self-parallel and hybrid-parallel, and their dynamics are analyzed by phase diagram, LEs, permutation entropy (PE) complexity and statistic$\chi ^{2}$. The simulation results show that the proposed maps have large maximum Lyapunov exponent (MLE), PE complexity, and uniform distribution. In particular, HD-MPPCMs have some interesting characteristics, such as full positive LEs, hyperchaotic behavior, global chaos, and full attractor distribution, which are the potential model for engineering applications. To further verify the practicability, the proposed maps are implemented on DSP platform, and applied to pseudo-random number generator (PRNG).

To improve the dynamical behaviors of 1D chaotic maps, a new linear-delay-modulation method (LDM) is proposed. Derived from the Sine map, a delayed Sine map (DSM) is proposed based on the LDM. Then, we substitute the Sine map in the SIMM system with DSM and obtained a delayed SIMM system (DSIMM). Its chaotic performance is analyzed through the phase diagram, Lyapunov exponent spectrum, and complexity. The results show that the delayed chaotic map can generate more complex dynamical behaviors and more random sequences. Hence, we apply the two delayed systems to a novel image encryption algorithm with the permutation-confusion-diffusion architecture. Firstly, to permutate the pixel of the image efficiently, the plain-image is scrambled by using a multilayer of the nonlinear index. Secondly, the image is confused by using the chaotic matrix generated with two chaotic sequences, and then, the ciphertext is transformed into a 1D sequence. Finally, to improve the plaintext sensitivity and facilitate key management, we enhance the sensitivity by applying a novel diffusion algorithm instead of using plaintext-related keystream. The diffusion equation contains the sum of undiffused pixels and the operation of cyclic bit-shift. Simulation results for the gray image illustrate the effectiveness of the proposed encryption algorithm.

In this letter, a localization algorithm, which combines energy operator with Doppler processing, is proposed for Doppler radar human sensing applications. For this algorithm, the energy operator is first used to extract the target components of interest from radar echoes and estimate their instantaneous frequencies (IFs). Then, on the basis of the IF estimation result, Doppler processing is applied to synthesize the target movement trajectories. Compared with the traditional localization methods, the proposed algorithm can more precisely estimate the target movement trajectory. Besides, it can further avoid the frequency ambiguity issue, and thus can be very promising for multitarget sensing applications. Experimental results are shown to demonstrate the performance of the proposed algorithm..

In this letter, a localization algorithm, which combines Hough transform and Doppler processing, is proposed for Doppler radar human sensing applications. The Hough transform is first applied to extract the interested target components and real-time estimate their instantaneous frequencies (IFs). Then, based on the IF estimation result, the target movement trajectories are synthesized by Doppler processing. To improve the detection accuracy and robustness, a generalized linear model is proposed for Hough transform, which can achieve highly dimensional frequency fitting to compensate the nonlinear error, meanwhile maintaining a low level of computational complexity for real-time processing. Experimental results are presented to illustrate the performance of the proposed algorithm.