Hartley's Algorithm Research Articles

High-security multidimensional data protection system based on the Hartley algorithm-driven chaotic scheme

This paper proposes a high-security multidimensional data protection system based on the Hartley algorithm-driven chaotic scheme. We utilize the fast Hartley algorithm instead of the fast fourier computation, and we employ chaotic sequences generated by the multi-winged chaotic system to achieve chaos-driven 3D constellation mapping, effectively integrating the chaotic system with the stochastic amplitude modulator. We reduce the signal’s peak-to-average power ratio (PAPR) by deploying a random amplitude modulator. Simultaneously, this approach enhances the security of the physical layer of the signal. The PAPR reduction can reach up to 2.6 dB, while the most robust and stable modulator scheme can gain 2 dB. Finally, in the Hartley frequency domain, the signal's frequency is disrupted, providing the entire system with a key space of 10131 to resist violent cracking and thus improving the system's overall security. To validate the feasibility of our scheme in comparison to conventional IFFT-based encrypted 3D orthogonal frequency division multiplexing, We achieved a transmission rate of 27.94 Gb/s over a 2 km multicore fiber. Experimental results show that since the random amplitude generator effectively reduces PAPR, our proposed encryption scheme increases the forward error correction threshold range by 1.1 dB, verifying that our proposed scheme has highly reliable security performance.

Non‐linear calibration optimisation based on the Levenberg–Marquardt algorithm

An outstanding calibration algorithm is the most important factor that affects the precision of attitude measurement. This study proposes a non-linear optimisation algorithm to refine the solutions of the initial guess obtained using the Zhang's technique, the Bouget's technique, or the Hartley's algorithm. Large sets of point correspondences were adopted to test the validity of the proposed method. Extensive practical experiments demonstrated that the proposed method can significantly improve the accuracy of calibration and ultimately obtains higher measurement precision. The error of the reprojection in the proposed method was <0.13 px. At a range of 1 m, the error rate was 0.5% for the length test and about 3% for the angle test. This study proposes a new method to calibrate the relationship between laser radar and the camera. Binocular vision was used to reconstruct the point cloud of the non-cooperative target. At the same time, data was also obtained using laser radar. Finally, the two groups of systems were fused. Accurate and dense three-dimensional information of the target was obtained. It could not only obtain the dense pose information of the target surface but also the texture and colour feature information of the target surface.

고품질의 3D 콘텐츠 제작을 위한 베이지안 접근방식의 사진측량기반 편위수정기법 개발

This paper proposes a photogrammetric rectification method based on Bayesian approach as a method that eliminates vertical parallax between stereo images to minimize visual fatigue of 3D contents. The image rectification consists of two phases; geometry estimation and epipolar transformation. For geometry estimation, coplanarity-based relative orientation algorithm was used in this paper. To ensure robustness for mismatch and localization error occurred by automation of tie point extraction, Bayesian approach was applied by introducing several prior constraints. As epipolar transformation perspective transformation was used based on condition of collinearity to minimize distortion of result images and modification for input images. Other algorithms were compared to evaluate performance. For geometry estimation, traditional relative orientation algorithm, 8-points algorithm and stereo calibration algorithm were employed. For epipolar transformation, Hartley algorithm and Bouguet algorithm were employed. The evaluation results showed that the proposed algorithm produced results with high accuracy, robustness about error sources and minimum image modification.

Newton-Raphson and Related Algorithms for Maximum Likelihood Variance Component Estimation

Three general purpose algorithms for maximum likelihood estimation of mean and variance components in mixed analysis of variance models are discussed. These are the Newton-Raphson algorithm, the Fisher scoring algorithm, and the Hemmerle and Hartley algorithm. Derivations for the first two are given and a unified presentation of all three makes some theoretical and practical comparisons possible. In addition the results of applying all three to a sequence of five problems are presented. The W transform of Hemmerle and Hartley is used throughout to reduce the computational burden associated with maximum likelihood variance component algorithms. The algorithms provide a unified approach to estimation and testing in the general mixed analysis of variance model.