Fast J-linkage algorithm for camera orientation applications

In this paper, a fast three-dimensional wavenumber scaling algorithm (3-D WSA) was proposed to reconstruct the wideband terahertz (THz) holographic image under the illumination of THz Gaussian beams. By taking the cross-range range coupling term into account, which is usually neglected in some fast algorithm, such as the enhanced phase shift migration (EPSM), the focusing depth for image reconstruction is greatly improved in the proposed fast algorithm. To deal with the cross-range range coupling term, a wavenumber scaling operator (WSO) was developed in the proposed algorithm which only uses chirp multiplications and FFTs with high computation efficiency and free of interpolation. Theoretical analysis on the relationship of the WSO parameters to the resulted maximum non-ambiguous range in holography image reconstruction was carried out with a quantitative criterion derived to appropriately determine the WSO parameters. Simulation results validate the proposed focusing algorithm and indicate that 3D WSA offers almost the same focusing capabilities as the most rigid phase shift migration (PSM) algorithm, meanwhile with computation efficiency greatly improved. Proof-of-principle experiments in 0.2 THz band were also performed based on a monostatic prototype imager. The experimental results demonstrate the effectiveness and the efficiency of the 3D WSA proposed in this paper.

Mining big data in brain imaging genetics is an emerging topic in brain science. It can uncover meaningful associations between genetic variations and brain structures and functions. Sparse canonical correlation analysis (SCCA) is introduced to discover bi-multivariate correlations with feature selection. However, these SCCA methods cannot be directly applied to big brain imaging genetics data due to two limitations. First, they have cubic complexity in the size of the matrix involved and are computational and memory intensive when the matrix becomes large. Second, the parameters in an SCCA method need to be fine-tuned in advance. This further dramatically increases the computational time, and gets severe in high-dimensional scenarios. In this paper, we propose two fast and efficient algorithms to speed up the structure-aware SCCA (S2CCA) implementations without modification to the original SCCA models. The fast algorithms employ a divide-and-conquer strategy and are easy to implement. The experimental results, compared with conventional algorithms, show that our algorithms reduce the time usage significantly. Specifically, the fast algorithms improve the computational efficiency by tens to hundreds of times compared to conventional algorithms. Besides, our algorithms yield similar correlation coefficients and canonical loading profiles to the conventional implementations. Our fast algorithms can be easily parallelized to further reduce the computational time. This indicates that the proposed fast scalable SCCA algorithms can be a powerful tool for big data analysis in brain imaging genetics.

Various deconvolution algorithms for acoustic source are developed to improve spatial resolution and suppress sidelobe of the conventional beamforming. To improve the computational efficiency and solution convergence of deconvolution, this paper proposes a Fourier-based improved fast iterative shrinkage thresholding algorithm. Simulations and experiments show that Fourier-based improved fast iterative shrinkage thresholding algorithm can achieve excellent acoustic identification performance, with high computational efficiency and good convergence. For Fourier-based improved fast iterative shrinkage thresholding algorithm, the larger the weight coefficient, the narrower the mainlobe width, and the better the convergence, but the spurious source also increases. The recommended weight coefficient for the array described herein is 3. In addition, like other Fourier-based deconvolution algorithms, Fourier-based improved fast iterative shrinkage thresholding algorithm using irregular focus grid can obtain better acoustic source identification performance than using the conventional regular focus grid.

Fringe pattern profilometry (FPP) is one of the most promising 3D profile measurement techniques, which has been widely applied in many areas. A challenge problem associated with FPP is the unwrapping of wrapped phase maps resulted from complex object surface shapes. Although existing quality-guided phase unwrapping algorithms are able to solve such a problem, they are usually extensively computational expensive and not able to be applied to fast 3D measurement scenarios. This paper proposes a new quality-guided phase unwrapping algorithm with higher computational efficiency than the conventional ones. In the proposed method, a threshold of quality value is used to classify pixels on the phase maps into two types: high quality (HQ) pixels corresponding to smooth phase changes and low quality (LQ) ones to rough phase variance. In order to improve the computational efficiency, the HQ pixels are unwrapped by a computationally efficient fast phase unwrapping algorithm, and the LQ pixels are unwrapped by computational expensive flood-fill algorithm. Experiments show that the proposed approach is able to recover complex phase maps with the similar accuracy performance as the conventional quality-guided phase unwrapping algorithm but is much faster than the later.

In this paper, a fast algorithm to reconstruct the spectrum of non-uniformly sampled signals is proposed. Compared with the original algorithm, the fast algorithm has a higher computational efficiency, especially when sampling sequence is long. Particularly, a transformation matrix is built, and the reconstructed spectrum is perfectly synthesized from the spectrum of every sampling channel. The fast algorithm has solved efficiency issues of spectrum reconstruction algorithm, and making it possible for the actual application of spectrum reconstruction algorithm in multi-channel Synthetic Aperture Radar (SAR).

In quasi-optical systems, the beam distortion and the cross-polarisation caused by the offset reflectors get a lot of attention. Also, the distortion was thoroughly studied in previous works. In this study, the authors give the detailed derivations of exact and approximate expressions of cross-polarisation and some simple explanations about energy conversion between co- and cross-polarisations in two-dimensional multi-reflector systems based on Gaussian beam mode analysis (GBMA). In addition two dual-reflector systems with different structures are used to verify the correctness and accuracy of GBMA comparing with physical optics (PO) in commercial software GRASP10. Furthermore, the authors fabricate one of these two systems and give experimental results. Then, they propose a fast analysis algorithm based on the vector addition method to reduce the computation time by eliminating the redundant modes. The efficiency of the proposed fast algorithm is illustrated by the number of modes and the calculation time for each reflection in a tri-reflector system. In addition, the authors also present the computation time of PO in GRASP10 as a reference. With the application of the fast algorithm, the computational efficiency is improved and increases with the number of reflections.

Adopting MPC to a fast changing system has always been a challenging problem due to the computational efficiency. As the equivalent aggregation strategy and the fast algorithm based on MPC proposed, the computational efficiency has been promoted greatly. This paper improves the fast algorithm combined with the equivalent aggregation strategy, and investigates the feasibility of adopting the fast algorithm based on MPC to DC-AC power inverter system which switching time is 52 μs. Simulation and the experiment have verified that the fast online MPC algorithm applied on the fast sampling system with embedded platform implementation works well.

Fast and memory-efficient algorithms for computing quadratic time–frequency distributions

The oversampled Gabor transform is more effective than the critically sampled one in many applications. The biorthogonality relationship between the analysis window and the synthesis window of the Gabor transform represents the completeness condition. However, the traditional discrete cosine transform (DCT)-based real-valued discrete Gabor transform (RGDT) is available only in the critically sampled case and its biorthogonality relationship for the transform has not been unveiled. To bridge these important gaps, this paper proposes a novel DCT-based RDGT, which can be applied in both the critically sampled case and the oversampled case, and their biorthogonality relationships can be derived. The proposed DCT-based RDGT involves only real operations and can utilize fast DCT algorithms for computation, which facilitates computation and implementation by hardware or software as compared to that of the traditional complex-valued discrete Gabor transform. This paper also develops block time-recursive algorithms for the efficient and fast computation of the RDGT and its inverse transform. Unified parallel lattice structures for the implementation of these algorithms are presented. Computational complexity analysis and comparisons have shown that the proposed algorithms provide a more efficient and faster approach for discrete Gabor transforms as compared to those of the existing discrete Gabor transform algorithms. In addition, an application in the noise reduction of the nuclear magnetic resonance free induction decay signals is presented to show the efficiency of the proposed RDGT for time-frequency analysis.

Most of the fast search motion estimation algorithms reduce the computational complexity of motion estimation (ME) greatly by checking only a few search points inside the search area. In this paper, we propose a new algorithm--multi-layer motion estimation (MME) which reduces the computational complexity of each distortion measure instead of reducing the number of search points. The conventional fast search motion estimation algorithms perform ME on the reference frame with full distortion measure; on the contrary, the MME performs ME on the layers with partial distortion measures to enhance the computational speed of ME. A layer is an image which is derived from the reference frame; each macro-pixel value in the layer represents the sum of the values of the corresponding pixels in the reference frame. A hierarchical quad-tree structure is employed in this paper to construct multiple layers from the reference frame. Experimental results on different video sequences show evidence that many motion vectors have been found similar both in the reference frame and the layers. The effectiveness of the proposed MME algorithm is compared with that of some state-of-the-art fast block matching algorithms with respect to speed and motion prediction quality. Experimental results on a wide variety of video sequences show that the proposed algorithm outperforms the other popular conventional fast search motion estimation algorithms computationally while maintaining the motion prediction quality very close to the full-search algorithm. Moreover, the proposed algorithm can achieve a maximum of 97.99 % speed-improvement rate against the fast full-search motion estimation algorithms which are based on hierarchical block matching process. The proposed MME performs the motion estimation on the layers by using three types of search patterns. The derivation of these search patterns exploits the characteristic of the center-biased motion vector distribution and that of less intensive block distortion measurement of the layers.

Interpreting datasets containing inertial data (acceleration, rate-of-turn, magnetic flux) requires a description of the datasets itself. Often this description is unstructured, stored as a convention or simply not available anymore. In this note, we argue that each modality exhibits particular statistical properties, which allows to reconstruct it solely from the sensor's data. To investigate this, tri-axial inertial sensor data from five publicly available datasets were analysed. Three statistical properties: mode, kurtosis, and number of modes are shown to be sufficient for classification - assuming the sampling rate and sample format are known, and that both acceleration and magnetometer data is present. While those assumption hold, 98% of all 1003 data points were correctly classified.

Synthetic Aperture Sonar (SAS) achieves high-resolution imaging of underwater targets by moving a small-aperture array and coherently processing the signals, thereby generating a virtual large-aperture imaging effect. The conventional Range-Doppler (RD) algorithm offers advantages such as high computational efficiency, simple processing flow, and ease of engineering implementation. However, due to the limitations of its underlying assumptions and its heavy reliance on interpolation for range walk correction, the RD algorithm cannot achieve both imaging efficiency and accuracy. To address this issue, this paper proposes an improved RD algorithm based on a sparse Bayesian learning two-dimensional deconvolution (SBL-dCv2) framework. The mathematical model of the algorithm is derived, and comparative simulation experiments with the traditional RD algorithm are conducted for multi-point targets. The simulation results show that the SBL-dCv2 improved RD algorithm delivers both high resolution and computational efficiency, offers stronger resolution performance for multi-point targets, and is better suited for practical engineering applications of SAS.

Most of the fast search motion estimation algorithms reduce the computational cost of motion estimation greatly by checking only a few search points inside the search area by using full distortion measure. This paper proposes a multi-layer motion estimation which employs partial distortion as its distortion measure to reduce the number of computations involved in each search point instead of reducing the number of search points. A layer is an image which is derived from the reference frame such that the sum of a block of pixels in the reference frame determines the point of a layer. It has been noticed on different video sequences that many motion vectors on the layers are the same as those searched on the reference frame. Experimental results on a wide variety of video sequences show that the proposed algorithm outperforms the other popular conventional fast search motion estimation algorithms computationally while maintaining the motion prediction quality very close to the full-search algorithm.

This paper presents a computationally efficient pipeline to achieve 3D point cloud reconstruction from video sequences. This pipeline involves a key frame selection step to improve the computational efficiency by generating reliable depth information from pair-wise frames. An outlier removal step is then applied in order to further improve the computational efficiency. The reconstruction is achieved based on a new absolute camera pose recovery approach in a computationally efficient manner. This pipeline is devised for both sparse and dense 3D reconstruction. The results obtained from video sequences exhibit higher computational efficiency and lower re-projection errors of the introduced pipeline compared to the existing pipelines.

Study DesignCase Study.ObjectivesEstablish the role a single inertial sensor may play in the objective quantification of dynamic postural stability following acute ankle injuries.BackgroundThe Y Balance test (YBT) is one of...