Adda Ali-Pacha

This paper presents a secure and effi-cient chaos-based pseudo-random number generator (PRNG) optimized for low-power, resource-constrained platforms. A lightweight, self-adaptive Perturbation-Randomization mechanism is embedded into the Tin-kerbell map to mitigate precision-induced degrada-tion and boost entropy, without increasing complex-ity. A hardware-efficient post-processing unit further enhances output randomness.Extensive evaluation confirms compliance with NIST SP800-22, Dieharder, and TestU01, while nonlinear measures (LLE, PE, SampEn, RQA) highlight strong dynamical complexity. Implemented on an Artix-7 FPGA, the design achieves 2.33 Gbps throughput at just 0.115 W power. Compared to ex-isting works, it offers full statistical validation, low area (227 LUTs, 232 FFs, 16 DSPs), and proven IoT suitability—unlike many prior designs that lack empir-ical support.

This paper introduces a novel chaos-driven adaptive polar coding scheme designed to enhance both the reliability and security of digital communications. The proposed system employs a two-dimensional (2D) hyperchaotic map to dynamically control coding parameters at the block level. For each block, the chaotic system generates two indices: one selects the block length from a predefined set of sizes 2^n, and the other selects a signal-to-noise ratio (SNR) value from a fixed SNR vector of length 1×11. The selected SNR value guides the calculation of the Bhattacharyya parameter, enabling optimized frozen bit selection during polar code construction. A shared secret key, composed of the chaotic map’s initial conditions and the SNR vector, ensures that the receiver can accurately regenerate the parameter sequence without any explicit exchange of configuration data. The system is evaluated over an additive white Gaussian noise (AWGN) channel using BPSK modulation. Simulation results demonstrate a clear improvement in bit error rate (BER) performance and enhanced security compared to conventional static polar coding. Furthermore, sensitivity analysis shows that decoding fails completely in the presence of key mismatch, thus ensuring strong data confidentiality. This approach offers an efficient and secure communication framework, particularly suited to dynamic and resource-constrained environments such as satellite systems and wireless sensor networks.

Mitigating Wormhole Attacks’ Risks within Wearable Body Network

In today’s world of unmanned aerial vehicle (UAV) telecommunications, data transmission is both important and delicate. Various organizations have used special techniques to ensure reliable transmission and stable communication. At this stage, we propose a method that enhances security and makes the transmission of data and image more resistant to interference and eavesdropping while improving signal reliability; this method is based on the Direct Sequence-Spread Spectrum (DS-SS) technique with a new pseudo-noise PN sequence created by merging chaotic and Barker sequences. MATLAB simulates these results for different spreading factors of the new sequence, and we evaluate the performance of the system in a user context on the Additive White Gaussian Noise (AWGN) channel. As a result, the new DS-SS technology improves the quality of data transmitted by UAVs.

The availability of hyperspectral images has significantly facilitated clay minerals identification and mapping. Based on the Hyperion L1R hyperspectral dataset, this research aims to improve previous scientific work related to identifying and mapping clay minerals in the Djebel Meni region (northwestern of Algeria). Firstly, we enhance the dataset quality through pre-processing techniques like the removal of bad bands, radiometric calibration, and atmospheric correction. Secondly, the Spectral Information Divergence (SID) algorithm was employed by introducing endmembers derived with the Sequential Maximum Angle Convex Cone (SMACC) algorithm initially and then using Jet Propulsion Laboratory (JPL) spectral signatures of Illite, Kaolinite, and Montmorillonite. The classification results show a correspondence between areas occupied by endmembers and JPL spectral signatures, which helps in matching endmembers with specific minerals. Finally, we conducted a comparative analysis of the two classifications outcomes against a reference map. This last is generated using SID algorithm, which takes ground truth spectral signatures as input. Our proposed approach using the SID classifier has given an overall accuracy score of 97.13% and 84.17% using endmembers image and JPL library respectively. The Kappa coefficient is respectively, with endmembers image and JPL, 0.93 and 0.57. These results show that the SID classification with the endmembers image is better than the SID classification with the JPL library because extracting the endmembers from the image is more accurate than doing that from the pure minerals analyzed in the laboratory. These promising results suggest that our approach could be extended to the identification of clay minerals in diverse hyperspectral datasets.