In this Letter, we present a novel design method of image-side telecentric freeform imaging systems. The freeform surfaces in the system can be generated using a point-by-point design approach starting from an initial...In this Letter, we present a novel design method of image-side telecentric freeform imaging systems. The freeform surfaces in the system can be generated using a point-by-point design approach starting from an initial system consisting of simple planes. The proposed method considers both the desired object–image relationships and the telecentricity at the image-side during the design process. The system generated by this method can be taken as a good starting point for further optimization. To demonstrate the benefit and feasibility of our method,we design two freeform off-axis three-mirror image-side telecentric imaging systems in the visible band. The systems operate at F/1.9 with a 30 mm entrance pupil diameter and 5° diagonal field-of-view. The modulation-transfer-function curves are above 0.69 at 100 lps/mm.展开更多
An optimized Neumann series（NS） approximation is described based on Frobenius matrix decomposition, this method aims to reduce the high complexity, which caused by the large matrix inversion of detection algorithm i...An optimized Neumann series（NS） approximation is described based on Frobenius matrix decomposition, this method aims to reduce the high complexity, which caused by the large matrix inversion of detection algorithm in the massive multiple input multiple output（MIMO） system. The large matrix in the inversion is decomposed into the sum of the hollow matrix and a Frobenius matrix, and the Frobenius matrix has the diagonal elements and the first column of the large matrix. In order to ensure the detection performance approach to minimum mean square error（MMSE） algorithm, the first three terms of the series approximation are needed, which results in high complexity as O（K;）, where K is the number of users. This paper further optimize the third term of the series approximation to reduce the computational complexity from O（K;） to O（K;）. The computational complexity analysis and simulation results show that the performance of proposed algorithm can approach to MMSE algorithm with low complexity O（K;）.展开更多
文摘In this Letter, we present a novel design method of image-side telecentric freeform imaging systems. The freeform surfaces in the system can be generated using a point-by-point design approach starting from an initial system consisting of simple planes. The proposed method considers both the desired object–image relationships and the telecentricity at the image-side during the design process. The system generated by this method can be taken as a good starting point for further optimization. To demonstrate the benefit and feasibility of our method,we design two freeform off-axis three-mirror image-side telecentric imaging systems in the visible band. The systems operate at F/1.9 with a 30 mm entrance pupil diameter and 5° diagonal field-of-view. The modulation-transfer-function curves are above 0.69 at 100 lps/mm.
文摘An optimized Neumann series（NS） approximation is described based on Frobenius matrix decomposition, this method aims to reduce the high complexity, which caused by the large matrix inversion of detection algorithm in the massive multiple input multiple output（MIMO） system. The large matrix in the inversion is decomposed into the sum of the hollow matrix and a Frobenius matrix, and the Frobenius matrix has the diagonal elements and the first column of the large matrix. In order to ensure the detection performance approach to minimum mean square error（MMSE） algorithm, the first three terms of the series approximation are needed, which results in high complexity as O（K;）, where K is the number of users. This paper further optimize the third term of the series approximation to reduce the computational complexity from O（K;） to O（K;）. The computational complexity analysis and simulation results show that the performance of proposed algorithm can approach to MMSE algorithm with low complexity O（K;）.
