- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Beam-Doppler Unitary ESPRIT for Multitarget DOA Estimation
摘要： High-resolution direction of arrival (DOA) estimation is a critical issue for mainbeam multitarget tracking in ground-based or airborne early warning radar system. A beam-Doppler unitary ESPRIT (BD-UESPRIT) algorithm is proposed to deal with this problem. Firstly, multiple snapshots without spatial aperture loss are obtained by using the technique of time-smoothing. Then the conjugate centrosymmetric discrete Fourier transform (DFT) matrix is used to transform the extracted data into beam-Doppler domain. Finally, the rotational invariance property of the space-time beam is exploited to estimate DOA of the target. The DOA estimation accuracy is improved greatly because the proposed algorithm takes full advantage of temporal information of the signal. Furthermore, the computational complexity of the presented algorithm is reduced dramatically, because the degree of freedom after beam transformation is very small and most of the operations are implemented in real-number domain. Numerical examples are given to verify the effectiveness of the proposed algorithm.
关键词： computational complexity,DOA estimation,time-smoothing,ESPRIT,beam-Doppler domain
更新于2025-09-23 15:22:29
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Quantum gate identification: Error analysis, numerical results and optical experiment
摘要： The identification of an unknown quantum gate is a significant issue in quantum technology. In this paper, we propose a quantum gate identification method within the framework of quantum process tomography. In this method, a series of pure states are applied to the gate and then a fast state tomography on the output states is performed and the data are used to reconstruct the quantum gate. The algorithm has computational complexity O(d3) with the system dimension d. The identification approach is compared with the maximum likelihood estimation method for the running time, which shows an efficiency advantage of our method. An error upper bound is established for the identification algorithm and the robustness of the algorithm against impurities in the input states is also tested. We perform a quantum optical experiment on a single-qubit Hadamard gate to verify the effectiveness of the identification algorithm.
关键词： Quantum system,Quantum tomography,Quantum gate identification,Computational complexity
更新于2025-09-23 15:22:29
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Direct Laser Writing of Mid-Infrared Straight and Bent Waveguides
摘要： The Hausdorff distance (HD) between two point sets is a commonly used dissimilarity measure for comparing point sets and image segmentations. Especially when very large point sets are compared using the HD, for example when evaluating magnetic resonance volume segmentations, or when the underlying applications are based on time critical tasks, like motion detection, then the computational complexity of HD algorithms becomes an important issue. In this paper we propose a novel efficient algorithm for computing the exact Hausdorff distance. In a runtime analysis, the proposed algorithm is demonstrated to have nearly-linear complexity. Furthermore, it has efficient performance for large point set sizes as well as for large grid size; performs equally for sparse and dense point sets; and finally it is general without restrictions on the characteristics of the point set. The proposed algorithm is tested against the HD algorithm of the widely used national library of medicine insight segmentation and registration toolkit (ITK) using magnetic resonance volumes with extremely large size. The proposed algorithm outperforms the ITK HD algorithm both in speed and memory required. In an experiment using trajectories from a road network, the proposed algorithm significantly outperforms an HD algorithm based on R-Trees.
关键词： computational complexity,algorithm,evaluation,Hausdorff distance,runtime analysis
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE 16th International Conference on Group IV Photonics (GFP) - Singapore, Singapore (2019.8.28-2019.8.30)] 2019 IEEE 16th International Conference on Group IV Photonics (GFP) - Enhanced Kerr Optical Nonlinearity of Waveguides Integrated with Graphene Oxide
摘要： The Hausdorff distance (HD) between two point sets is a commonly used dissimilarity measure for comparing point sets and image segmentations. Especially when very large point sets are compared using the HD, for example when evaluating magnetic resonance volume segmentations, or when the underlying applications are based on time critical tasks, like motion detection, then the computational complexity of HD algorithms becomes an important issue. In this paper we propose a novel efficient algorithm for computing the exact Hausdorff distance. In a runtime analysis, the proposed algorithm is demonstrated to have nearly-linear complexity. Furthermore, it has efficient performance for large point set sizes as well as for large grid size; performs equally for sparse and dense point sets; and finally it is general without restrictions on the characteristics of the point set. The proposed algorithm is tested against the HD algorithm of the widely used national library of medicine insight segmentation and registration toolkit (ITK) using magnetic resonance volumes with extremely large size. The proposed algorithm outperforms the ITK HD algorithm both in speed and memory required. In an experiment using trajectories from a road network, the proposed algorithm significantly outperforms an HD algorithm based on R-Trees.
关键词： computational complexity,algorithm,evaluation,Hausdorff distance,runtime analysis
更新于2025-09-23 15:21:01
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[IEEE 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM) - Sheffield (2018.7.8-2018.7.11)] 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM) - A Multi-Channel Partial-Update Algorithm for Sea Clutter Suppression in Passive Bistatic Radar
摘要： Sea clutters with Doppler-varying spectrum exert a notable negative impact on the detection performance, especially with low-velocity targets, when a passive bistatic radar is employed to detect sea-surface targets. One feasible solution is to modulate the reference signal onto the Doppler dimension and, as such, a filter with a wide notch and sharp edges can be obtained to suppress the residual clutters. However, to achieve this goal, a considerably high computational complexity is demanded in the existing two-dimensional (2D) adaptive filters. In this context, a novel 2D partial-update normalized least mean square algorithm is proposed to reduce the computational load, as well as to yield a superior clutter suppression performance. The effectiveness of the proposed approach is verified by real-data experiments.
关键词： Passive bistatic radar (PBR),multi-channel filtering,adaptive cancellation,sea clutter,computational complexity
更新于2025-09-23 15:21:01
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Transmission Characteristics and Fano-like Lineshape in Coupled-slotted Microresonators
摘要： A new unique class of foldable distance transforms of digital images (DT) is introduced, baptized: Fast exact euclidean distance (FEED) transforms. FEED class algorithms calculate the DT starting directly from the de?nition or rather its inverse. The principle of FEED class algorithms is introduced, followed by strategies for their ef?cient implementation. It is shown that FEED class algorithms unite properties of ordered propagation, raster scanning, and independent scanning DT. Moreover, FEED class algorithms shown to have a unique property: they can be tailored to the images under investigation. Benchmarks are conducted on both the Fabbri et al. data set and on a newly developed data set. Three baseline, three approximate, and three state-of-the-art DT algorithms were included, in addition to two implementations of FEED class algorithms. It illustrates that FEED class algorithms i) provide truly exact Euclidean DT; ii) do no suffer from disconnected Voronoi tiles, which is a unique feature for non-parallel but fast DT; iii) outperform any other approximate and exact Euclidean DT with its time complexity OeNT, even after their optimization; and iv) are unequaled in that they can be adapted to the characteristics of the image class at hand.
关键词： Fast exact euclidean distance (FEED),computational complexity,distance transformation,distance transform,benchmark,adaptive,Voronoi
更新于2025-09-23 15:19:57
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Quasi-Two-Stage Multi-Functional Photovoltaic Inverter with Power Quality Control and Enhanced Conversion Efficiency
摘要： The Hausdorff distance (HD) between two point sets is a commonly used dissimilarity measure for comparing point sets and image segmentations. Especially when very large point sets are compared using the HD, for example when evaluating magnetic resonance volume segmentations, or when the underlying applications are based on time critical tasks, like motion detection, then the computational complexity of HD algorithms becomes an important issue. In this paper we propose a novel efficient algorithm for computing the exact Hausdorff distance. In a runtime analysis, the proposed algorithm is demonstrated to have nearly-linear complexity. Furthermore, it has efficient performance for large point set sizes as well as for large grid size; performs equally for sparse and dense point sets; and finally it is general without restrictions on the characteristics of the point set. The proposed algorithm is tested against the HD algorithm of the widely used national library of medicine insight segmentation and registration toolkit (ITK) using magnetic resonance volumes with extremely large size. The proposed algorithm outperforms the ITK HD algorithm both in speed and memory required. In an experiment using trajectories from a road network, the proposed algorithm significantly outperforms an HD algorithm based on R-Trees.
关键词： evaluation,computational complexity,Hausdorff distance,algorithm,runtime analysis
更新于2025-09-19 17:13:59
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Multi-Pulse Fitting of Transition Edge Sensor Signals from a Near-Infrared Continuous-Wave Source
摘要： A new unique class of foldable distance transforms of digital images (DT) is introduced, baptized: Fast exact euclidean distance (FEED) transforms. FEED class algorithms calculate the DT starting directly from the de?nition or rather its inverse. The principle of FEED class algorithms is introduced, followed by strategies for their ef?cient implementation. It is shown that FEED class algorithms unite properties of ordered propagation, raster scanning, and independent scanning DT. Moreover, FEED class algorithms shown to have a unique property: they can be tailored to the images under investigation. Benchmarks are conducted on both the Fabbri et al. data set and on a newly developed data set. Three baseline, three approximate, and three state-of-the-art DT algorithms were included, in addition to two implementations of FEED class algorithms. It illustrates that FEED class algorithms i) provide truly exact Euclidean DT; ii) do no suffer from disconnected Voronoi tiles, which is a unique feature for non-parallel but fast DT; iii) outperform any other approximate and exact Euclidean DT with its time complexity OeNT, even after their optimization; and iv) are unequaled in that they can be adapted to the characteristics of the image class at hand.
关键词： benchmark,computational complexity,Fast exact euclidean distance (FEED),distance transform,Voronoi,distance transformation,adaptive
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Investigation of Radiative Coupling from InGaAsP Quantum Wells for Improving End-of-Life (EOL) Efficiency in Multijunction Solar Cells
摘要： A new unique class of foldable distance transforms of digital images (DT) is introduced, baptized: Fast exact euclidean distance (FEED) transforms. FEED class algorithms calculate the DT starting directly from the de?nition or rather its inverse. The principle of FEED class algorithms is introduced, followed by strategies for their ef?cient implementation. It is shown that FEED class algorithms unite properties of ordered propagation, raster scanning, and independent scanning DT. Moreover, FEED class algorithms shown to have a unique property: they can be tailored to the images under investigation. Benchmarks are conducted on both the Fabbri et al. data set and on a newly developed data set. Three baseline, three approximate, and three state-of-the-art DT algorithms were included, in addition to two implementations of FEED class algorithms. It illustrates that FEED class algorithms i) provide truly exact Euclidean DT; ii) do no suffer from disconnected Voronoi tiles, which is a unique feature for non-parallel but fast DT; iii) outperform any other approximate and exact Euclidean DT with its time complexity OeNT, even after their optimization; and iv) are unequaled in that they can be adapted to the characteristics of the image class at hand.
关键词： Fast exact euclidean distance (FEED),computational complexity,distance transformation,distance transform,benchmark,adaptive,Voronoi
更新于2025-09-19 17:13:59
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Room-temperature power-stabilized narrow-linewidth tunable erbium-doped fiber ring laser based on cascaded Mach-Zehnder interferometers with different free spectral range for strain sensing
摘要： A new unique class of foldable distance transforms of digital images (DT) is introduced, baptized: Fast exact euclidean distance (FEED) transforms. FEED class algorithms calculate the DT starting directly from the de?nition or rather its inverse. The principle of FEED class algorithms is introduced, followed by strategies for their ef?cient implementation. It is shown that FEED class algorithms unite properties of ordered propagation, raster scanning, and independent scanning DT. Moreover, FEED class algorithms shown to have a unique property: they can be tailored to the images under investigation. Benchmarks are conducted on both the Fabbri et al. data set and on a newly developed data set. Three baseline, three approximate, and three state-of-the-art DT algorithms were included, in addition to two implementations of FEED class algorithms. It illustrates that FEED class algorithms i) provide truly exact Euclidean DT; ii) do no suffer from disconnected Voronoi tiles, which is a unique feature for non-parallel but fast DT; iii) outperform any other approximate and exact Euclidean DT with its time complexity OeNT, even after their optimization; and iv) are unequaled in that they can be adapted to the characteristics of the image class at hand.
关键词： benchmark,computational complexity,Fast exact euclidean distance (FEED),distance transform,Voronoi,distance transformation,adaptive
更新于2025-09-19 17:13:59