Efficient Matching Research Articles

Adaptive Non-linear Pattern Matching Automata

Efficient pattern matching is fundamental for practical term rewrite engines. By preprocessing the given patterns into a finite deterministic automaton the matching patterns can be decided in a single traversal of the relevant parts of the input term. Most automaton-based techniques are restricted to linear patterns, where each variable occurs at most once, and require an additional post-processing step to check so-called variable consistency. However, we can show that interleaving the variable consistency and pattern matching phases can reduce the number of required steps to find all matches. Therefore, we take the existing adaptive pattern matching automata as introduced by Sekar et al and extend these with consistency checks. We prove that the resulting deterministic pattern matching automaton is correct, and show several examples where some reduction can be achieved.

Research on the Supply Chain Problems and Optimization of Live Streaming E-commerce

The acceleration of the pace of mobile Internet construction and the outbreak of the COVID-19 epidemic have created new opportunities for the development of the live broadcasting economy. More and more businesses provide consumers with more visual and personalized services through live broadcasting. While developing the rapid development of live streaming e-commerce, it also puts forward new requirements for the whole supply chain. The optimization of the supply chain can comprehensively improve the operation efficiency of enterprises in all links. This article combines the meaning and basic structure of the live streaming e-commerce supply chain combined with the concept of live streaming e-commerce and supply chain. Compared with the traditional supply chain, the live streaming e-commerce supply chain has high information interaction efficiency, supply and demand matching efficiency and service efficiency. The article believes that the supply chain facing supply and undertaking ability, response speed and low coordination and sharing of supply chain should build reverse integrated supply chain; strengthen supply chain information sharing through digital technology, ensure production stability, improve the service quality of all links of the supply chain, and enhance competitiveness.

Learning interlaced sparse Sinkhorn matching network for video super-resolution

How to effectively fuse inter- and intra-frame spatio-temporal information plays a key role in video super-resolution (VSR). Most existing works rely heavily on the accuracy of motion estimation and compensation for spatio-temporal feature alignment. However, they cannot perform well when suffering from large-scale and complex motions. To this end, this paper introduces an efficient and effective Interlaced Sparse Sinkhorn Matching (ISSM) network for VSR, which aligns supporting frames with the reference one in the feature space by learning optimal matching between image regions across frames. Specifically, the ISSM divides the input dense affinity matrix into two sparse block matrixes: one can match long-distance regions while the other can match short-distance regions, and then we leverage an efficient Sinkhorn method on each block to learn optimal matching. Moreover, we insert a residual atrous spatial pyramid pooling module before the ISSM, which can flexibly generate multi-scale features frame by frame to capture the multi-scale context information in images. The aligned features of each adjacent frame are then fed to a bidirectional temporal fusion module to capture the rich temporal information. Finally, the fused features are sent into a frame-wise dynamic reconstruction network to produce an HR frame. Extensive evaluations on three benchmark datasets demonstrate the superiority of our method over the state-of-the-art methods in terms of PSNR and SSIM.

Content-based Image Retrieval for Map Georeferencing

Abstract. In recent years, libraries have made great progress in digitising troves of historical maps with high-resolution scanners. Providing user-friendly information access for cultural heritage through spatial search and webGIS requires georeferencing of the hundreds of thousands of digitised maps.Georeferencing is usually done manually by finding “ground control points”, locations in the digital map image, whose identity is unambiguous and can easily be found in modern-day reference geodata/mapping data. To decide whether two symbols from different maps describe the same object, their semantic and spatial relations need to be matched. Automating this process is the only feasible way to georeference the immense quantities of maps in conceivable time. However, automated solutions for spatial matching quickly fail when faced with incomplete data – which is the greatest challenge when comparing maps of different ages or scales.These problems can be overcome by computing map similarity in the image domain. Treating maps as a special case of image processing allows efficient and robust matching and thus identification of geographical regions without the need to explicitly model semantics. We propose a method to encode worldwide reference VGI mapping data as image features, allowing the construction of an efficient lookup index. With this index, content-based image retrieval can be used for both geolocating a given map for georeferencing with high accuracy. We demonstrate our approach on hundreds of map sheets of different historical topographical survey map series, successfully georeferencing most of them within mere seconds.

3D Chiral MetaCrystals

AbstractFine control of the chiral light–matter interaction at the nanoscale, by exploiting designed metamaterial architecture, represents a cutting‐edge craft in the field of biosensing, quantum, and classic nanophotonics. Recently, artificially engineered 3D nanohelices demonstrate programmable wide chiroptical properties by tuning materials and architecture, but fundamental diffractive aspects that are at the origin of chiral resonances still remain elusive. Here, a novel concept of a 3D chiral metacrystal, where the chiroptical properties are finely tuned by in‐plane and out‐of‐plane diffractive coupling, is proposed. Different chiral dipolar modes can be excited along the helix arms, generating far field optical resonances and radiation pattern with in‐plane side lobes, and suggesting that a combination of efficient dipole excitation and diffractive coupling matching controls the collective oscillations among the neighbor helices. The proposed concept of compact chiral metacrystal can be suitable for integration with quantum emitters and open perspectives in novel schemes of enantiomeric detection.

Ferromagnetic Resonance-Enhanced Electrically Small Antennas

It is proposed that ferromagnetic resonance (FMR) of thin-film ferrites can be utilized to simultaneously improve the radiation efficiency and input impedance matching of electrically small antennas (ESAs). To validate the concept, the role of FMR in radiation is first derived analytically with an ideal thin-film ferrite radiator. It was concluded that the Gilbert damping of the ferrite, determining the quality factor of FMR, directly impacts on the radiation efficiency of the antenna. A practical example is proposed in the form of an electrically small single-loop antenna loaded with a thin-film yttrium-iron-garnet (YIG) core. The prototype has been designed, fabricated, and evaluated through both full-wave simulations and experiments. The simulation results match well to the experimental results, demonstrating the efficacy and significance of the idea. In addition, broadband equivalent circuit models are derived to model both the electrically small loops with and without the FMR enhancement and to provide additional insights into the design. The circuit models prove to be effective in predicting the input impedance and radiation efficiency of FMR-enhanced ESAs at a precision comparable with full-wave simulations.

Kernelized Lagrangian particle tracking

In this article, we present a novel Lagrangian particle tracking method derived from the perspective of the tracking-by-detection paradigm that has been adopted by many vision tracking tasks. Under this paradigm, the particle tracking problem consists of first learning a function (the tracker) that maps the target particle’s image projection backwardly to its possible position inferred from its precedent tracking information. The target particle’s actual position is then detected by applying the learned function to particle images captured by cameras. We also propose to solve the function learning problem using kernel methods. The proposed method is therefore named Kernelized Lagrangian particle tracking (KLPT). The current state-of-art LPT approach Shake-The-Box (STB), despite equipping a highly efficient image matching and shaking-based optimization procedure, tends to be trapped by local minimum when dealing with challenging cases featuring sparse temporal data, data extracted from complex flows and noisy data. KLPT can overcome these optimization difficulties since it features a highly robust function learning procedure combined with an efficient linear optimization technique. We assessed our proposed KLPT against various STB implementations both on synthetic and real experimental datasets. For the synthetic dataset depicting a turbulent cylinder wake-flow at Re=3900, we focused on studying the effects of particle density, time separation, and image noise. KLPT outperformed STB in all cases by tracking more particles and producing more accurate particle fields. This performance gain, compared to STB, is more prominent for the dataset with larger seeding density, time separation, and more noise. For real experimental data on an impinging jet flow in a water tank, KLPT can capture longer tracks and provides more detailed flow reconstruction at highly turbulent regions than STB. Overall, comparison to STB shows significant improvements in accuracy (lower reconstruction positional error) and robustness (more and longer tracks). We finally show the results of KLPT on the 1st LPT challenge datasets. Our algorithm has achieved state-of-the-art performance with particle images up to 0.08 ppp (particles per pixel).

Abstract 10484: Cardiac Adaptation to Acute Haemodynamic Stress in Patients with Type 2 Diabetes

Background: Due to varying cardiac workloads, efficient demand-supply matching is crucial for maintaining normal cardiac function. Cardiac metabolism is profoundly affected by changes in cardiac workload. There is scarcity of non-invasive studies evaluating how the heart adapts to hemodynamic stress in type 2 diabetes (T2D) patients. Objectives: Using cardiovascular magnetic resonance (CMR) and 31 Phosphorus MR spectroscopy ( 31 P-MRS) we assessed changes in cardiac energetics, perfusion, strain, systolic and diastolic function in response to increases in cardiac workload with dobutamine stress in T2D patients and healthy volunteers (HV). Methods: 24 T2D patients and 18 matched HV were recruited. Participants with ischemic heart disease were excluded. Results: Table-1 shows clinical and CMR/ 31 P-MRS data. Compared to HV, T2D patients showed reductions in left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) at rest, and a blunted increase in LVEF and GLS during stress. Diastolic function was impaired in T2D with lower mitral in-flow E/A ratio compared to HV at rest, which was further reduced during stress in both groups. Stress myocardial blood flow was lower in T2D patients.Compared to HV, rest phosphocreatinine (PCr)/ATP ratio was 17% lower in T2D patients, with a further 8% reduction during stress. However, there was a more pronounced reduction in the PCr/ATP with stress in HV (33% relative reduction), although stress PCr/ATP remained numerically higher in HV compared to T2D patients. Conclusions: T2D is associated with blunted ability of the myocardium to significantly increase contractile function from rest to stress. Moreover, T2D patients show exacerbated reductions in myocardial energetics and diastolic function during stress. Although reductions in myocardial energetics and diastolic function in response to acute stress were also detected in HV, these assessments remained higher in HV at stress compared to T2D patients.

Efficient probability-oriented feature matching using wide field-of-view imaging

Feature matching is a key technique for a wide variety of computer vision and image processing applications such as visual localization. It permits finding correspondences of significant points within the environment that eventually determine the localization of a mobile agent. In this context, this work evaluates an Adaptive Probability-Oriented Feature Matching (APOFM) method that dynamically models the visual knowledge of the environment in terms of the probability of existence of features. Several improvements are proposed to achieve a more robust matching in a visual odometry framework: a study on the classification of the matching candidates, enhanced by a nearest neighbour search policy; a dynamic weighted matching that exploits the probability of feature existence in order to tune the matching thresholds; and an automatic false positive detector. Additionally, a comparison of performance is carried out, considering a publicly available dataset composed of two kinds of wide field-of-view images: catadioptric and fisheye. Overall, the results validate the appropriateness of these contributions, which outperform other well-recognized implementations within this framework, such as the standard visual odometry, a visual odometry method based on RANSAC, as well as the basic APOFM. The analysis shows that fisheye images provide more visual information of the scene, with more feature candidates. Contrarily, omnidirectional images produce fewer feature candidates, but with higher ratios of feature acceptance. Finally, it is concluded that improved precision is obtained when the location problem is solved by this method.

Privacy‐preserving and efficient user matching based on attribute encryption in mobile social networks

SummaryWith the rapid popularity of social networking platforms, users can be matched when sharing their profiles. However, there is a risk of leakage of sensitive user information during the user matching process, which leads to the lack of user privacy protection. In this paper, we propose a privacy protection scheme based on the encryption of hidden attributes during user matching in mobile social networks, which uses linear secret sharing scheme (LSSS) as the access structure based on ciphertext policy attribute‐based encryption (CP‐ABE), and the match server can perform friend recommendation by completing bi‐directional attribute matching determination without disclosing user attribute information. In addition, the use of selective keywords protects the privacy of requesters and publishers in selecting keywords and selecting plaintext attacks. The scheme reduces the encryption and decryption overhead for users by dividing encryption into a preparation phase and an online phase and shifting most of the decryption overhead from the requester to the match server. The experimental results show that the scheme ensures user privacy while effectively reducing communication overhead.

Hybrid physics and data-driven modeling for unconventional field development and its application to US onshore basin

The objective of this study is to develop a hybrid model by combining physics and data-driven approach for optimum unconventional field development. We used physics-based reservoir simulations to generate training datasets. These uncalibrated priors were incorporated into data-driven machine learning (ML) algorithms so that the algorithms can learn the underlying physics between reservoir simulation input and output, cumulative oil production for parent and child wells. The reservoir simulations in a single half-cluster model consists of injection and production phases. This allows the algorithm to relate the impact of completion design, well spacing and timing to the production profiles. After performing a sensitivity analysis to reduce the number of training inputs, more than 20,000 simulations results were generated as the training data. The best accuracy, R2 = 0.94, was achieved with the neural network model after tuning hyper-parameters. Then, we incorporated the hybrid model with the genetic algorithm to perform efficient history matching to calibrate model parameters and predict estimated ultimate recovery (EUR) and net present value with 10% discount rate (NPV10). The great efficiency in the hybrid model takes only several minutes to complete a single well history matching. The calibrated model shows that the prediction from the hybrid model is physically meaningful compared to actual reservoir simulations, capturing the impact of fracture geometry, child well spacing and timing on the well production. With multiple history matching results, we populated value acreage maps (e.g., EUR and NPV10) which can benefit the unconventional field development planning. Lastly, a blind test was conducted in an area of interest to demonstrate that the proposed workflow can effectively predict a well performance in the area without sufficient data, utilizing calibrated model parameters from history matched results of adjacent wells. The proposed model can provide quick and scalable solutions that honor underlying physics to help decision making on unconventional field development based on production profile and economic metrics with various completion designs.

An efficient eigenmode restoration and mode matching method for multi‐junctional inhomogeneous waveguiding structures based on the Krylov subspace method

Mode matching is a widely used method in various electromagnetic problems due to its dimensional advantages. However, the numerical implementation of mode matching is challenged by its high computational complexity in solving eigenvalue problems. The Lanczos method approximates the original eigenvalue problem in an efficient way and transforms the mode matching into an implicit form. The price of efficiency enhancement is the loss of eigenmode accuracy and behaviour interpretation. Its numerical efficiency is also influenced by block matrix dimensions while solving multi-junctional waveguide structures. This work proposes a novel structure eigenmode restoration algorithm based on the Krylov subspace method. The method takes the conventional explicit mode matching strategy, but the efficiency enhancement for solving eigenmodes is prominent. The overall computational complexity is reduced from the original O(N3) to O(N1.5) without the cost of eigenmode accuracy loss. Numerical benchmark cases verify the proposed method’s accuracy and efficiency. The efficiency advantage is outstanding especially in computational time. Extra discussions about eigenmode selection strategies based on accuracy controlling indicate that the proposed method possesses better flexibility in solving complicated waveguide problems.

A comparative molecular structure – NLO activity study of ortho-bridged dibenzaldehydes: Synthesis, crystal structure, SHG, and DFT studies

Structural and second order nonlinear optical characteristics of four ortho-bridged dibenzaldehydes with alkyloxy- and xylenyloxy linkers are compared from the XRD, UV-Vis-NIR spectroscopic, SHG, and DFT studies. Interruption of electron polarization by π-conjugation of the linkers between the two benzaldehyde units affect their NLO properties like hyperpolarizability (β) in these acceptor–π–bridge–donor–π–bridge–acceptor (A–π–D–π–A) systems. The noncentrosymmetric 2,2′-[1,2-phenylenebis(methylenoxy)]dibenzaldehyde exhibits SHG efficiency matching with that of potassium dihydrogenphosphate (KDP) in Kurtz-Perry powder method. The acentric supramolecular architecture stabilized by H-bonding and aromatic π-π stacking interactions in the solid state with the non-linear o-xylenyloxy linker rationalizes the origin of its structure driven NLO property. Chromophore density, broad optical transparency, low cut-off wavelength of electronic absorptions, and thermal stability of 2,2′-[1,2-phenylenebis(methylenoxy)]dibenzaldehyde make it a promising candidate for second order NLO applications.

A 23 dBm Gain Shaping Stacked Power Block CMOS Power Amplifier Achieving 36% PAE

This paper introduces a design methodology that reduces the fundamental trade-off between linearity and power added efficiency (PAE) in CMOS power amplifier (PA). In our work, a stacked power block (SPB) has been proposed to mitigate the effect of gate–source capacitance (Cgs), thus linearizing the PA. Each stage is biased independently to shape the gain profile, either to be in the expanded mode or in the compressed mode, in which once combined it delivers a flat gain response and confirming the linearity performance. Efficient PI input and output matching networks are proposed to ensure no further distortion, once connected to the 50 Ω source and load. The PA achieves input and output return losses of less than −10 dB from 2.40 to 2.50 GHz. At the center frequency of 2.45 GHz, the SPB-PA achieves a gain of 10 dB and it is unconditionally stable. The proposed gain shaping linearization technique delivers a maximum linear output power (Poutlinmax) of 19.8 dBm with only 3.3 dB back-off from maximum output power (Poutmax) of 23.1 dBm. The SPB-PA meets the WLAN specification with linear PAE of 30% and peak PAE of 36.1%. The proposed SPB-PA reduces the fundamental trade-off between linearity and efficiency. Integration of this PA in wireless SoC shall reduce the chip’s overall power consumption.

Season- and Trend-aware Symbolic Approximation for Accurate and Efficient Time Series Matching

Processing and analyzing time series datasets have become a central issue in many domains requiring data management systems to support time series as a native data type. A core access primitive of time series is matching, which requires efficient algorithms on-top of appropriate representations like the symbolic aggregate approximation (SAX) representing the current state of the art. This technique reduces a time series to a low-dimensional space by segmenting it and discretizing each segment into a small symbolic alphabet. Unfortunately, SAX ignores the deterministic behavior of time series such as cyclical repeating patterns or a trend component affecting all segments, which may lead to a sub-optimal representation accuracy. We therefore introduce a novel season- and a trend-aware symbolic approximation and demonstrate an improved representation accuracy without increasing the memory footprint. Most importantly, our techniques also enable a more efficient time series matching by providing a match up to three orders of magnitude faster than SAX.

A robust end-to-end deep learning framework for detecting Martian landforms with arbitrary orientations

With increasingly massive amounts of high-resolution images of Mars, automated detection of geological landforms on Mars has received widespread interest. It is significant for acquiring knowledge of distant planetary surfaces and processes, or manifold onboard applications such as spacecraft motion estimation and obstacle avoidance. This is a challenging task, not only because of the multiple sizes of targets and complex image backgrounds, but also the various orientations of some bar-shaped landforms in satellite images captured from the top view. The existing methods for directed landform detection require several pre or post-processing operations to extract possible regions of interest and final detection results with orientation, which are very time consuming. In this paper, a new end-to-end deep learning framework is developed for detecting arbitrarily-directed landforms. This framework, named Rotated-SSD (Single Shot MultiBox Detector, SSD), can locate and identify different landforms on Mars in one pass, by using rotatable anchor-box based mechanism. To enhance its robustness against angle variation of the targets and complex backgrounds, a new efficient match strategy is proposed for anchoring default boxes to ground truth boxes in the model training process and an autoencoder-based unsupervised pre-training operation is introduced to improve both the model training and inference performance. The proposed framework is tested for detection of bar-shaped buttes and dark slope streaks on satellite images. The detection results show that our framework can significantly contribute to onboard motion estimation systems. The comparative results demonstrate that the proposed match strategy outperforms other state-of-the-art match strategies with regard to model training efficiency and prediction accuracy. The pre-training strategy can facilitate the training of deep architectures in case of limited available training data.

Topology Aware Object-Level Semantic Mapping Towards More Robust Loop Closure

Loop closure can effectively eliminate the accumulated error and plays an important role in Simultaneous Localization and Mapping (SLAM). There remains challenges in loop detection and loop correction due to the large viewpoints difference and the environment appearance changes. In this letter, we propose a novel loop closure method based on object modeling and semantic graph matching. We use voxels and cuboids to model the object-level features in the environment, and further represent the environment as a semantic graph with topological information. On this basis, an efficient graph matching method based on edit distance is proposed for robust place recognition. Finally, the loop correction is carried out by object alignment between semantic maps. Experimental results demonstrate that the proposed method can realize omni-directional loop closure without viewpoint constraint, and is robust to environmental appearance changes. Compared with the existing appearance-based methods, the performance has been greatly improved.

2D materials–based homogeneous transistor-memory architecture for neuromorphic hardware

In neuromorphic hardware, peripheral circuits and memories based on heterogeneous devices are generally physically separated. Thus, exploration of homogeneous devices for these components is key for improving module integration and resistance matching. Inspired by the ferroelectric proximity effect on two-dimensional (2D) materials, we present a tungsten diselenide–on–lithium niobate cascaded architecture as a basic device that functions as a nonlinear transistor, assisting the design of operational amplifiers for analog signal processing (ASP). This device also functions as a nonvolatile memory cell, achieving memory operating (MO) functionality. On the basis of this homogeneous architecture, we also investigated an ASP-MO integrated system for binary classification and the design of ternary content-addressable memory for potential use in neuromorphic hardware.

Integrated Pose Estimation Using 2D Lidar and INS Based on Hybrid Scan Matching.

Point cloud data is essential measurement information that has facilitated an extended functionality horizon for urban mobility. While 3D lidar and image-depth sensors are superior in implementing mapping and localization, sense and avoidance, and cognitive exploration in an unknown area, applying 2D lidar is inevitable for systems with limited resources of weight and computational power, for instance, in an aerial mobility system. In this paper, we propose a new pose estimation scheme that reflects the characteristics of extracted feature point information from 2D lidar on the NDT framework for exploiting an improved point cloud registration. In the case of the 2D lidar point cloud, vertices and corners can be viewed as representative feature points. Based on this feature point information, a point-to-point relationship is functionalized and reflected on a voxelized map matching process to deploy more efficient and promising matching performance. In order to present the navigation performance of the mobile object to which the proposed algorithm is applied, the matching result is combined with the inertial navigation through an integration filter. Then, the proposed algorithm was verified through a simulation study using a high-fidelity flight simulator and an indoor experiment. For performance validation, both results were compared and analyzed with the previous techniques. In conclusion, it was demonstrated that improved accuracy and computational efficiency could be achieved through the proposed algorithms.

Efficient match pair selection for matching large-scale oblique UAV images using spatial priors

ABSTRACT Image matching is critical for photorealistic 3D reconstruction based on oblique images. An efficient match pair selection methodology for matching large-scale oblique UAV images is proposed in this paper. The proposed methodology effectively uses existing geospatial data and prior knowledge about data acquisition to generate precise match pairs. First, the principal point of each image is directly georeferenced. The direct georeferencing is based on a novel new DEM-aided direct georeferencing algorithm which employs an iterative binary search in the elevation domain. Second, a terrain-adaptive search radius is calculated for each image based on the calculated ground points, POS data, and prior knowledge. Finally, initial match pairs are generated based on a camera-oriented approach. The camera-oriented approach generates match pairs using k nearest neighbours (knn) search and prior knowledge. False connections in the initial match pairs are filtered using geometrical constraints. A limited number of match pairs are kept to reduce the redundancy of the generated match graph. The proposed methodology is tested on a dataset containing 28,560 images. The experimental results show that the precision of the generated match pairs is 99.99%. An incremental 3D reconstruction of the scene is conducted based on the robustly matched images. More than 99.8% of all the images are successfully oriented. The profiling analysis shows that the proposed methodology generates all the match pairs in 7.719 seconds. The precision, efficiency, and robustness of the proposed methodology are comprehensively analysed. The experimental and simulation results show that the proposed methodology is efficient and precise for large-scale UAV photogrammetry.