The rise of 3-D data dependency across various fields, from medical imaging to virtual reality, requires robust security to prevent leakage or unauthorized access and to keep sensitive information secure from tampering. As 3-D models consist of floating-point numbers arranged in a specialized format, their protection necessitates a reliable encryption technique to safeguard sensitive data across various domains. In this paper, a symmetric encryption technique for 3-D models based on the quantum logistic map, Arnold cat map, improved logistic map, and DNA encoding is presented. Initially, the hash value derived from the SHA-256 function, combined with the plaintext data, is used as a symmetric key to establish the initial conditions for the chaotic system. Following this, the improved logistic map (ILM) is applied to introduce confusion among the coordinate values of plaintext data. Subsequently, the partially encrypted data is segmented into integer and fractional components. The integer matrix undergoes scrambling and diffusion through the complete framework of the quantum logistic map, Arnold cat map, DNA diffusion algorithm, and DNA complementary rule, enhancing the complexity of the encryption process. On the other hand, the fractional part is manipulated using the quantum logistic map. Finally, the transformed integer and fractional parts are systematically integrated to obtain the final encrypted data. The proposed algorithm undergoes rigorous testing on a variety of 3-D models, validating the effectiveness of the proposed encryption scheme by offering a high entropy value close to 8, 100% NPCR and 33.37% UACI values, near-zero correlation, strong resistance against differential attack, maintained computational efficiency, and high key sensitivity. Furthermore, encryption time varies linearly with the size of input 3-D model, demonstrating the applicability of the proposed algorithm for large-scale 3-D datasets.

Abstract The electrocardiogram (ECG) is a diagnostic tool that records the electrical activity of the heart, providing information on the cardiac cycle. It is widely utilized in medical and healthcare settings for monitoring purposes. The QRS complex, which encompasses the Q wave, R wave, and S wave, is indicative of the depolarization of the ventricles. The QRS complex is a standard feature of ECG leads, and its waves vary depending on the lead position and the heart's electrical activity. The primary objective of this research endeavor has been to implement the Ahlstrom and Tompkins method's equations using Very High-Speed Integrated Circuit Hardware Description Language (VHDL). The aim is to develop a system capable of identifying the peak of two successive ECG waveforms. The hardware chip design for detecting the QRS complex in ECG signals was implemented using Xilinx ISE 14.7 and subsequently validated through successful simulation in Modelsim 10.0 software. The algorithm's performance is assessed on various FPGA platforms, specifically focusing on power consumption, latency, frequency, and hardware utilizations on Field-Programmable Gate Arrays (FPGAs) by Xilinx. The Virtex-7 FPGA has demonstrated superior performance when compared to other FPGA models, with an ideal delay value of 7.120 ns, power consumption of 1.95 mW, and operating frequency of 750 MHz. The novelty of this work lies in the scalable FPGA-based algorithm for QRS detection, which excels in switching speed and low power consumption across various FPGAs. This allows designers to integrate deep learning techniques for QRS detection and achieve hardware acceleration for real-time implementation.

Abstract The evolution of lane-keeping assistance systems (LKAS) has significantly contributed to enhancing road safety, driving comfort, and reducing driver workload. The automotive sector is moving towards a safer driving experience with autonomous agricultural vehicles in the field. The article intends to design and develop a system capable of detecting agricultural lanes and giving appropriate steering control outputs. The research aim is to build a system of Level-2 autonomous agricultural vehicle facilities for advanced driver assistance systems, and allow the driver to assist the vehicle in the left and right lane turns. The experimental model uses image processing to detect lanes on roads. The experimental results show that the LKAS model output is derived from an entry speed of 30 km/h to 60 km/h, a controller with a prediction horizon of 1.75 s to 2.5 s, and a control horizon of 0.5 to 0.75 s on a 3 km straight test track. The proposed approach has a 14% better performance in terms of execution time for steering inputs applied by the driver.