Fast Mapping Algorithm Research Articles

Fast mapping algorithm with optimized placement selection for CGRA

Fast mapping algorithm with optimized placement selection for CGRA

Quantitative characterization of fatigue damage in plate structures based on FSOM

For the problem of fatigue damage detection and damage degree assessment of plate structures, a quantitative damage assessment method based on the fast self-organizing feature mapping (FSOM) algorithm is proposed in this paper. The damage detection problem is transformed into a binary classification problem by extracting multidimensional damage features of the Lamb wave signal in plate to be detected and selecting damage sensitive features. Then, the FSOM network is used to identify the health state of the plate to be inspected, and the damage index is obtained by fusing the damage sensitive features using FSOM to quantitatively evaluate the damage level of the plate to be inspected. Simulation and experimental results show this method has a good dynamic tracking capability for the fatigue damage evolution of aluminum and composite plates, and can achieve quantitative assessment of fatigue damage of plate structures.

KngMap: Sensitive and Fast Mapping Algorithm for Noisy Long Reads Based on the K-Mer Neighborhood Graph

With the rapid development of single molecular sequencing (SMS) technologies such as PacBio single-molecule real-time and Oxford Nanopore sequencing, the output read length is continuously increasing, which has dramatical potentials on cutting-edge genomic applications. Mapping these reads to a reference genome is often the most fundamental and computing-intensive step for downstream analysis. However, these long reads contain higher sequencing errors and could more frequently span the breakpoints of structural variants (SVs) than those of shorter reads, leading to many unaligned reads or reads that are partially aligned for most state-of-the-art mappers. As a result, these methods usually focus on producing local mapping results for the query read rather than obtaining the whole end-to-end alignment. We introduce kngMap, a novel k-mer neighborhood graph-based mapper that is specifically designed to align long noisy SMS reads to a reference sequence. By benchmarking exhaustive experiments on both simulated and real-life SMS datasets to assess the performance of kngMap with ten other popular SMS mapping tools (e.g., BLASR, BWA-MEM, and minimap2), we demonstrated that kngMap has higher sensitivity that can align more reads and bases to the reference genome; meanwhile, kngMap can produce consecutive alignments for the whole read and span different categories of SVs in the reads. kngMap is implemented in C++ and supports multi-threading; the source code of kngMap can be downloaded for free at: https://github.com/zhang134/kngMap for academic usage.

High-Precision and Fast LiDAR Odometry and Mapping Algorithm

LiDAR SLAM technology is an important method for the accurate navigation of automatic vehicles and is a prerequisite for the safe driving of automatic vehicles in the unstructured road environment of complex parks. This paper proposes a LiDAR fast point cloud registration algorithm that can realize fast and accurate localization and mapping of automatic vehicle point clouds through a combination of a normal distribution transform (NDT) and point-to-line iterative closest point (PLICP). First, the NDT point cloud registration algorithm is applied for the rough registration of point clouds between adjacent frames to achieve a rough estimate of the pose of automatic vehicles. Then, the PLICP point cloud registration algorithm is adopted to correct the rough registration result of the point cloud. This step completes the precise registration of the point cloud and achieves an accurate estimate of the pose of the automatic vehicle. Finally, cloud registration is accumulated over time, and the point cloud information is continuously updated to construct the point cloud map. Through numerous experiments, we compared the proposed algorithm with PLICP. The average number of iterations of the point cloud registration between adjacent frames was reduced by 6.046. The average running time of the point cloud registration between adjacent frames decreased by 43.05156 ms. The efficiency of the point cloud registration calculation increased by approximately 51.7%. By applying the KITTI dataset, the computational efficiency of NDT-ICP was approximately 60% higher than that of LeGO-LOAM. The proposed method realizes the accurate localization and mapping of automatic vehicles relying on vehicle LiDAR in a complex park environment and was applied to a Small Cyclone automatic vehicle. The results indicate that the proposed algorithm is reliable and effective.

Motion Similarity Evaluation between Human and a Tri-Co Robot during Real-Time Imitation with a Trajectory Dynamic Time Warping Model.

Precisely imitating human motions in real-time poses a challenge for the robots due to difference in their physical structures. This paper proposes a human–computer interaction method for remotely manipulating life-size humanoid robots with a new metrics for evaluating motion similarity. First, we establish a motion capture system to acquire the operator’s motion data and retarget it to the standard bone model. Secondly, we develop a fast mapping algorithm, by mapping the BVH (BioVision Hierarchy) data collected by the motion capture system to each joint motion angle of the robot to realize the imitated motion control of the humanoid robot. Thirdly, a DTW (Dynamic Time Warping)-based trajectory evaluation method is proposed to quantitatively evaluate the difference between robot trajectory and human motion, and meanwhile, visualization terminals render it more convenient to make comparisons between two different but simultaneous motion systems. We design a complex gesture simulation experiment to verify the feasibility and real-time performance of the control method. The proposed human-in-the-loop imitation control method addresses a prominent non-isostructural retargeting problem between human and robot, enhances robot interaction capability in a more natural way, and improves robot adaptability to uncertain and dynamic environments.

Vision Models for Wide Color Gamut Imaging in Cinema.

Gamut mapping is the problem of transforming the colors of image or video content so as to fully exploit the color palette of the display device where the content will be shown, while preserving the artistic intent of the original content's creator. In particular, in the cinema industry, the rapid advancement in display technologies has created a pressing need to develop automatic and fast gamut mapping algorithms. In this article, we propose a novel framework that is based on vision science models, performs both gamut reduction and gamut extension, is of low computational complexity, produces results that are free from artifacts and outperforms state-of-the-art methods according to psychophysical tests. Our experiments also highlight the limitations of existing objective metrics for the gamut mapping problem.

The Ultrafast and Accurate Mapping Algorithm FANSe3: Mapping a Human Whole-Genome Sequencing Dataset Within 30\xa0Minutes

Aligning billions of reads generated by the next-generation sequencing (NGS) to reference sequences, termed “mapping”, is the time-consuming and computationally-intensive process in most NGS applications. A Fast, accurate and robust mapping algorithm is highly needed. Therefore, we developed the FANSe3 mapping algorithm, which can map a 30 × human whole-genome sequencing (WGS) dataset within 30 min, a 50 × human whole exome sequencing (WES) dataset within 30 s, and a typical mRNA-seq dataset within seconds in a single-server node without the need for any hardware acceleration feature. Like its predecessor FANSe2, the error rate of FANSe3 can be kept as low as 10–9 in most cases, this is more robust than the Burrows–Wheeler transform-based algorithms. Error allowance hardly affected the identification of a driver somatic mutation in clinically relevant WGS data and provided robust gene expression profiles regardless of the parameter settings and sequencer used. The novel algorithm, designed for high-performance cloud-computing after infrastructures, will break the bottleneck of speed and accuracy in NGS data analysis and promote NGS applications in various fields. The FANSe3 algorithm can be downloaded from the website: http://www.chi-biotech.com/fanse3/.

Towards Higher Performance and Robust Compilation for CGRA Modulo Scheduling

Coarse-Grained Reconfigurable Architectures (CGRA) is a promising solution for accelerating computation intensive tasks due to its good trade-off in energy efficiency and flexibility. One of the challenging research topic is how to effectively deploy loops onto CGRAs within acceptable compilation time. Modulo scheduling (MS) has shown to be efficient on deploying loops onto CGRAs. Existing CGRA MS algorithms still suffer from the challenge of mapping loop with higher performance under acceptable compilation time, especially mapping large and irregular loops onto CGRAs with limited computational and routing resources. This is mainly due to the under utilization of the available buffer resources on CGRA, unawareness of critical mapping constraints and time consuming method of solving temporal and spatial mapping. This article focus on improving the performance and compilation robustness of the modulo scheduling mapping algorithm for CGRAs. We decomposes the CGRA MS problem into the temporal and spatial mapping problem and reorganize the processes inside these two problems. For the temporal mapping problem, we provide a comprehensive and systematic mapping flow that includes a powerful buffer allocation algorithm, and efficient interconnection & computational constraints solving algorithms. For the spatial mapping problem, we develop a fast and stable spatial mapping algorithm with backtracking and reordering mechanism. Our MS mapping algorithm is able to map loops onto CGRA with higher performance and faster compilation time. Experiment results show that given the same compilation time budget, our mapping algorithm generates higher compilation success rate. Among the successfully compiled loops, our approach can improve 5.4 to 14.2 percent performance and takes x24 to x1099 less compilation time in average comparing with state-of-the-art CGRA mapping algorithms.

Fast hyperbolic mapping based on the hierarchical community structure in complex networks

The hyperbolic geometry of complex networks has recently emerged as a promising framework for the analysis of real and synthetic networks; one significant challenge of the hyperbolic geometry framework is the necessity to map large real networks to latent geometric spaces. Addressing this problem, we present a novel fast hyperbolic mapping algorithm called hyperbolic mapping based on the hierarchical community structure (HMCS), which is based on the network evolution model, the community-sector model in hyperbolic space and the hierarchical community structure (HCS) of complex networks. We present an index called community closeness (CC) to measure the adjacency relationship between the communities. Then we propose a ranking algorithm for first-level and second-level communities based on CC to determine the order of communities on the hyperbolic disc and map the network into hyperbolic space based on the order and the angular range of the corresponding sector of the second-level communities. We find that HMCS greatly reduces the mapping time complexity and achieves linearity () in sparse networks under the premise of ensuring better mapping accuracy compared with existing hyperbolic mapping algorithms. Experiments show that the HMCS algorithm occupies a unique attractive position in the space of tradeoffs between mapping accuracy and computational complexity.

Global Tracking of Myocardial Motion in Ultrasound Sequence Images: A Feasibility Study.

The assessment of myocardial motion plays a promising role in the evaluation of cardiac function. This study aims to propose a novel framework of global estimation of the myocardial motion using radio-frequency (RF) data. The framework consists of B-mode image reconstruction, displacement estimation, myocardium extraction, and image fusion. The RF data of murine heart in parasternal long-axis (PLAX) view were collected for B-mode image reconstruction and displacement estimation. The vectorized normalized cross-correlation (VNCC) approach was proposed to globally estimate the displacements of the RF frames, while a sum-table based normalized cross-correlation (STNCC) was performed as reference algorithm. The bimodal fusion images were obtained to visualize the motion and anatomical structure of myocardium by an improved fast mapping algorithm (IFMA). In comparison with STNCC, the computation time of displacement using VNCC reduced by approximate 10s. The myocardial motions of anterior wall and posterior wall during one cardiac cycle were similarly tracked by VNCC as that of STNCC. The averaged absolute error in displacement between the two methods ranges from 1 to 3μm. The obtained myocardial elastographic images using VNCC intuitively present the morphological and mechanical changes during the contraction period of left ventricle. The results demonstrate that the proposed framework is an efficient tool for the estimation of myocardial motion reflecting cardiac systolic function. This approach has potentials to provide visualized information of myocardium for diagnosis and prognosis of cardiovascular diseases (CVDs).

Natural teaching for humanoid robot via human-in-the-loop scene-motion cross-modal perception

PurposeThis paper aims to present a human-in-the-loop natural teaching paradigm based on scene-motion cross-modal perception, which facilitates the manipulation intelligence and robot teleoperation.Design/methodology/approachThe proposed natural teaching paradigm is used to telemanipulate a life-size humanoid robot in response to a complicated working scenario. First, a vision sensor is used to project mission scenes onto virtual reality glasses for human-in-the-loop reactions. Second, motion capture system is established to retarget eye-body synergic movements to a skeletal model. Third, real-time data transfer is realized through publish-subscribe messaging mechanism in robot operating system. Next, joint angles are computed through a fast mapping algorithm and sent to a slave controller through a serial port. Finally, visualization terminals render it convenient to make comparisons between two motion systems.FindingsExperimentation in various industrial mission scenes, such as approaching flanges, shows the numerous advantages brought by natural teaching, including being real-time, high accuracy, repeatability and dexterity.Originality/valueThe proposed paradigm realizes the natural cross-modal combination of perception information and enhances the working capacity and flexibility of industrial robots, paving a new way for effective robot teaching and autonomous learning.

A dynamic neural network model for accelerating preliminary parameterization of 3D triangular mesh surfaces

This study proposes an effective and fast preliminary mapping algorithm for 3D triangular mesh surfaces. The proposed method exploits barycentric mapping theory and dynamic neural network for computing parametric coordinates corresponding to vertices of 3D triangular mesh. The dynamic network model iteratively moves internal nodes in 2D parametric space until they convergently reach an equilibrium state. The method effectively computes parametric space coordinates of large meshes (having more than 1.5 K vertices) in less time compared to the traditional method using inverse matrix calculation. The proposed method is tested on many surfaces of varying size, and experimental results prove its efficiency and efficacy.

Efficient Resource Allocation in Cognitive Networks

Cognitive radio (CR) in conjunction with multiple-input multiple-output (MIMO) orthogonal frequency-division multiple access is a candidate technology for future mobile radio networks. The short communication range of underlay CR systems is commonly a major limiting factor. In this paper, we propose a computationally and spectrally efficient resource allocation scheme for multiuser MIMO orthogonal-frequency-division-multiplexing-based underlay CR networks to provide good spectral efficiency gain and, therefore, increased communication range. Since the formulated optimization problem defines a mixed-integer programming (combinatorial task) that is hard to solve, we propose a two-phase scheme to produce efficient solutions for both the downlink and the uplink. Particularly, the first procedure elaborates on an adaptive precoding that is characterized by spectral efficiency due to the degrees of freedom it can provide. The second procedure develops a fast subcarrier mapping algorithm, which can be worked out through optimal power distribution among the CR users. The proposed scheme is optimal for the downlink but, however, near-optimal for the uplink. Simulation results demonstrate the bandwidth and computational efficiencies of the proposed scheme compared with the state of the art.

Subpixel Mapping Algorithms Based on Block Structural Self-Similarity Learning

Subpixel mapping (SPM) algorithms effectively estimate the spatial distribution of different land cover classes within mixed pixels. This paper proposed a new subpixel mapping method based on image structural self-similarity learning. Image structure self-similarity refers to similar structures within the same scale or different scales in image itself or its downsampled image, which widely exists in remote sensing images. Based on the similarity of image block structure, the proposed method estimates higher spatial distribution of coarse-resolution fraction images and realizes subpixel mapping. The experimental results show that the proposed method is more accurate than existing fast subpixel mapping algorithms.

A Strong Tracking Square Root Central Difference FastSLAM for Unmanned Intelligent Vehicle With Adaptive Partial Systematic Resampling

An improved fast simultaneous localization and mapping (FastSLAM) algorithm based on the strong tracking square root central difference Kalman filter (STSRCDKF) with adaptive partial systematic resampling is proposed in this paper to solve the large-scale simultaneous localization and mapping (SLAM) problem for unmanned intelligent vehicle. In the proposed algorithm, STSRCDKF is composed of a strong tracking filter and a square root central difference Kalman filter. STSRCDKF is used to design an adaptive adjusting proposal distribution of the particle filter and to estimate the Gaussian densities of the landmarks. Moreover, an adaptive partial systematic resampling operation is carried out to reduce the degree of particle degeneracy and maintain the diversity of particles. The performance of the proposed algorithm is compared with that of central difference FastSLAM and FastSLAM2.0; the simulation results based on the simulator and two benchmark data sets verify that the proposed algorithm has better adaptability and robustness to respond with time-varying measurement noise. In addition, it reduces computational cost and improves state estimation accuracy and consistency. Furthermore, the validity of the proposed algorithm is verified by the experimental result in campus test site of Beijing University of Technology.

A Fast Robot Identification and Mapping Algorithm Based on Kinect Sensor.

Internet of Things (IoT) is driving innovation in an ever-growing set of application domains such as intelligent processing for autonomous robots. For an autonomous robot, one grand challenge is how to sense its surrounding environment effectively. The Simultaneous Localization and Mapping with RGB-D Kinect camera sensor on robot, called RGB-D SLAM, has been developed for this purpose but some technical challenges must be addressed. Firstly, the efficiency of the algorithm cannot satisfy real-time requirements; secondly, the accuracy of the algorithm is unacceptable. In order to address these challenges, this paper proposes a set of novel improvement methods as follows. Firstly, the ORiented Brief (ORB) method is used in feature detection and descriptor extraction. Secondly, a bidirectional Fast Library for Approximate Nearest Neighbors (FLANN) k-Nearest Neighbor (KNN) algorithm is applied to feature match. Then, the improved RANdom SAmple Consensus (RANSAC) estimation method is adopted in the motion transformation. In the meantime, high precision General Iterative Closest Points (GICP) is utilized to register a point cloud in the motion transformation optimization. To improve the accuracy of SLAM, the reduced dynamic covariance scaling (DCS) algorithm is formulated as a global optimization problem under the G2O framework. The effectiveness of the improved algorithm has been verified by testing on standard data and comparing with the ground truth obtained on Freiburg University’s datasets. The Dr Robot X80 equipped with a Kinect camera is also applied in a building corridor to verify the correctness of the improved RGB-D SLAM algorithm. With the above experiments, it can be seen that the proposed algorithm achieves higher processing speed and better accuracy.

Fast Subpixel Mapping Algorithms for Subpixel Resolution Change Detection

Due to rapid changes on the Earth's surface, it is important to perform land cover change detection (CD) at a fine spatial and fine temporal resolution. However, remote sensing images with both fine spatial and temporal resolutions are commonly not available or, where available, may be expensive to obtain. This paper attempts to achieve fine spatial and temporal resolution land cover CD with a new computer technology based on subpixel mapping (SPM): The fine spatial resolution land cover maps (FRMs) are first predicted through SPM of the coarse spatial but fine temporal resolution images, and then, subpixel resolution CD is performed by comparison of class labels in the SPM results. For the first time, five fast SPM algorithms, including bilinear interpolation, bicubic interpolation, subpixel/pixel spatial attraction model, Kriging, and radial basis function interpolation methods, are proposed for subpixel resolution CD. The auxiliary information from the known FRM on one date is incorporated in SPM of coarse images on other dates to increase the CD accuracy. Based on the five fast SPM algorithms and the availability of the FRM, subpixels for each class are predicted by comparison of the estimated soft class values at the target fine spatial resolution and borrowing information from the FRM. Experiments demonstrate the feasibility of the five SPM algorithms using FRM in subpixel resolution CD. They are fast methods to achieve subpixel resolution CD.

Fast Mapping Algorithm from WSDL to OWL-S

Recently semantic web services represent the most technology developed for machine to machine interaction. The problem of discovering and selecting the most suitable web service represents a challenge for semantic web services. In this paper performance evaluation of mapping algorithm from web services annotations (WSDL) to semantic annotations (OWL-S) based on ontology search engine is presented. During mapping process primitive type remains without change. The complex type are converted to OWL ontology by extracted them and passing to ontology search and standardization process without need of conversion into temporary ontology. The keywords extracted in the linguistic search phase and are extended using word net. The mapping algorithm and its modification are implemented in Java and evaluated by 310 files WSDL. The output results of two algorithms are identical. But the proposed modified algorithm is faster than mapping algorithm.

On fast iterative mapping algorithms for stripe based coarse-grained reconfigurable architectures

Reconfigurable devices have potential for great flexibility/efficiency, but mapping algorithms onto these architectures is a long-standing challenge. This paper addresses this challenge for stripe based coarse-grained reconfigurable architectures (CGRAs) by drawing on insights from graph drawing. We adapt fast, iterative algorithms from hierarchical graph drawing to the problem of mapping to stripe based architectures. We find that global sifting is 98 times as fast as simulated annealing and produces very compact designs with 17% less area on average, at a cost of 5% greater wire length. Interleaving iterations of Sugiyama and global sifting is 40 times as fast as simulated annealing and achieves somewhat more compact designs with 1.8% less area on average, at a cost of only 1% greater wire length. These solutions can enable fast design space exploration, rapid performance testing, and flexible programming of CGRAs “in the field.”

Effect of Catheter Tip‐Tissue Surface Contact on Three‐Dimensional Left Atrial and Pulmonary Vein Geometries: Potential Anatomic Distortion of 3D Ultrasound, Fast Anatomical Mapping, and Merged 3D CT‐Derived Images

Anatomic Distortion of 3D Mapping. Although catheter tip-tissue contact is known as a reliable basis for mapping and ablation of atrial fibrillation (AF), the effects of different mapping methods on 3-dimensional (3D) map configuration remain unknown. Twenty AF patients underwent Carto-based 3D ultrasound (US) evaluation. Left atrium (LA)/pulmonary vein (PV) geometry was constructed with the 3D US system. The resulting geometry was compared to geometries created with a fast electroanatomical mapping (FAM) algorithm and 3D US merged with computed tomography (merged 3D US-CT). The 3D US-derived LA volumes were smaller than the FAM- and merged 3D US-CT-derived volumes (75 ± 21 cm(3) vs 120 ± 20 cm(3) and 125 ± 25 cm(3) , P < 0.0001 for both). Differences in anatomic PV orifice fiducials between 3D US- and FAM- and merged 3D US-CT-derived geometries were 6.0 (interquartile range 0-9.3) mm and 4.1 (0-7.0) mm, respectively. Extensive encircling PV isolation guided by 3D US images with real-time 2D intracardiac echocardiography-based visualization of catheter tip-tissue contact generated ablation point (n = 983) drop-out at 1.9 ± 3.8 mm beyond the surface of the 3D US-derived LA/PV geometry. However, these same points were located 1.5 ± 5.4 and 0.4 ± 4.1 mm below the FAM- and merged 3D US-CT-derived surfaces. Different mapping methods yield different 3D geometries. When AF ablation is guided by 3D US-derived images, ablation points fall beyond the 3D US surface but below the FAM- or merged 3D US-CT-derived surface. Our data reveal anatomic distortion of 3D images, providing important information for improving the safety and efficacy of 3D mapping-guided AF ablation. (J Cardiovasc Electrophysiol, Vol. 24, pp. 259-266, March 2013).