Matching Constraints Research Articles

Overtly Headed XPs and Irish Syntax-Prosody Mapping

Analyses of Irish phonological phrasing (Elfner 2012 et seq.) have been influential in shaping Match Theory (Selkirk 2011), an OT approach to mapping syntactic to prosodic structure. We solve two constraint ranking paradoxes concerning the relative ranking of Match and StrongStart. Irish data indicate that while XPs with silent heads can fail to map to phonological phrases in certain circumstances, overtly headed XPs cannot. They also indicate that rebracketing due to the constraint StrongStart occurs only sentence-initially, contrary to predictions. We account for these puzzles by invoking Van Handel's (2019) Match constraint which sees only XPs with overt heads, and by positing a new version of StrongStart which only applies to material at the left edge of the intonational phrase. Our analysis is developed using the Syntax-Prosody in Optimality Theory application (SPOT) and OTWorkplace.

Morphosyntax–phonology mismatches in Muskogee

The mismatching of morphosyntactic and phonological domains inside words provides a testing ground for models of the morphosyntax–phonology interface. This paper describes a pattern of morphosyntax–phonology mismatches in Muskogee. Muskogee verbs are spelled out at two phases, vP and CP, resulting in two phonological domains, which this paper models as ω-recursion. The vP phase and ωmin are mismatched: either vP-phase material is parsed outside ωmin – an undermatch – or CP-phase material is parsed inside ωmin – an overmatch. The mismatch pattern requires a parallel model of morphosyntax–phonology mapping to distinguish mismatches using gradient Align constraints, rather than categorial Match constraints. Additionally, a phase-based model must allow earlier cycles to be altered in later cycles, ruling out strict phase inalterability in phonology, while a Stratal OT analysis must send a word's first phase through the stem-level phonology, regardless of its ultimate phasal structure.

Robust Optical and SAR Image Registration Based on OS-SIFT and Cascaded Sample Consensus

Although several algorithms have achieved automatic registration on optical and synthetic aperture radar (SAR) images, it is still a challenge to establish enough reliable correspondences between such images due to their different imaging mechanisms. For this purpose, we propose a robust point-feature-based registration method. Considering the inherent properties of optical image and SAR image, two different gradient operators are utilized to construct scale spaces and extract features. A new gradient operator is defined for SAR image, yielding a more consistent gradient with optical image gradient calculated by the multiscale Sobel operator. Then, a novel cascaded matching method called cascaded sample consensus (CSC) is put forward to increase the number of correct correspondences. In the first matching, a simple but effective scale constraint strategy is used to remove outliers for a robust initial transformation model. Considering the spatial location relationship in each correct matching pair, the second matching constructs precise search spaces of the best matching points for more correspondences. Experimental results finally verify the robustness and accuracy of the proposed algorithm.

Fixing Defect of Photometric Loss for Self-Supervised Monocular Depth Estimation

View-synthesis-based methods have shown very promising results for the task of unsupervised depth estimation in single images. Most existing approaches synthesize a new image and employ it as the supervision signal for depth and pose prediction. There are two problems in these approaches: 1) There are many combinations of pose and depth that can synthesize a certain new image; therefore, reconstructing the depth and pose based on the view-synthesis method from only two images is an inherently ill-posed problem; 2) The model is trained under the photometric consistency assumption that the brightness or gradient is constant when applied to the video sequences. However, this assumption is easily violated in realistic scenes due to light changes, reflective surfaces and occlusions. To overcome the first drawback, we exploit the point cloud consistency constraint to eliminate ambiguity. To overcome the second drawback, we use threshold masks to filter dynamic and occluded points and introduce matching point constraints that implicitly encode the geometry relationship between two matched points to improve the precision of depth prediction. In addition, we employ epipolar constraints to compensate for the instability of the photometric error in textureless regions and varying illumination conditions. The experimental results on the KITTI, Cityscapes and NYUv2 datasets show that the method can improve the accuracy of depth prediction and enhance the robustness of the model in handling textureless regions and illumination changes. The code and data are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/XTUPRLAB/FixUnDepth</uri> .

Reconfigurable electronics by disassembling and reassembling van der Waals heterostructures

Van der Waals heterostructures (vdWHs) have attracted tremendous interest owing to the ability to assemble diverse building blocks without the constraints of lattice matching and processing compatibility. However, once assembled, the fabricated vdWHs can hardly be separated into individual building blocks for further manipulation, mainly due to technical difficulties in the disassembling process. Here, we show a method to disassemble the as-fabricated vdWHs into individual building blocks, which can be further reassembled into new vdWHs with different device functionalities. With this technique, we demonstrate reconfigurable transistors from n-type to p-type and back-gate to dual-gate structures through re-stacking. Furthermore, reconfigurable device behaviors from floating gate memory to Schottky diode and reconfigurable anisotropic Raman behaviors have been obtained through layer re-sequencing and re-twisting, respectively. Our results could lead to a reverse engineering concept of disassembled vdWHs electronics in parallel with state-of-the-art vdWHs electronics, offering a general method for multi-functional pluggable electronics and optoelectronics with limited material building blocks.

New floating/grounded FDNC and non-ideal grounded FDNR simulators based on VDTA

The paper proposes a new grounded/floating frequency dependent negative conductance (FDNC) simulator, and a non-ideal frequency dependent negative resistance (FDNR) simulator, based on deployment of voltage differencing transconductance amplifiers (VDTA), along with two grounded capacitances. The presented simulators feature electronic controllability of realized negative conductance/resistance via DC bias current of VDTA, with no component matching constraints. The non-ideality analysis of the circuits is also presented. The proposed solutions exhibit an excellent behaviour under non-ideal conditions with low active/passive sensitivity. The working of the presented simulators has been verified by corresponding voltage mode filter design examples, and has been verified by HSPICE simulations which were carried out on the basis of 0.18 μm CMOS technology.

Entity alignment via knowledge embedding and type matching constraints for knowledge graph inference

The prosperous development of cognition and human-level intelligence have made the creation of large-scale domain knowledge graphs (KGs) a hot research field. Entity alignment (EA), which discovers entity pairs that represent the same real object in different KGs, is the key technology for building large scale knowledge graph. Existing EA methods rely mainly on the vector representation of the entity and ignoring the semantic features of an entity in a KG, result in poor alignment quality. To address this limitation, we propose a new EA framework based on knowledge embeddings (KEs) and type matching constraints. By embedding two KGs into a unified vector space, we combine the similarity of the entity vector and the degree of the entity type matching to perform more accurate cross-KG EA. The experimental results show that our proposed method has very good performance on both cross-KG and cross-language data sets. On the DBP-YG dataset, in terms of Hit@1, Hits@10, and MRR, our methods have an average increase of 27% over the baseline method MTransE. In most cases, our method significantly improves the accuracy of EA compared with state-of-the-art methods.

Supplementary DDCC+ based universal filter with grounded passive elements

A new voltage-mode (VM) analog filter is proposed in this paper. The proposed circuit is composed of three plus-type single output DDCCs and only grounded passive elements that are important when the integrated circuit fabrication is involved. It has three high input impedances and one low output impedance. As a result, it has the feature of easy cascadability with other VM structures. Passive element matching constraints and extra additional active devices are not needed for it. Non-ideality analyses such as non-ideal gain and parasitic impedance effects are also given. Many simulations through SPICE program and several experimental studies are included.

An Enhanced Transition Model for Unsupervised Localization

Unsupervised localization based on received signal strength and inertial measurement unit (RSS + IMU) sequences is an important branch of indoor localization community, among which Transitional Model to predict Motion from signal change (TMM) is a promising method that does not require much prior knowledge, e.g., floor maps. However, there are still many challenging problems existing in TMM. Among them, the computation burden of the model is awfully heavy, and its localization error is also a painful point. In order to solve the above challenges, we propose a novel transition model, called enhanced TMM (ETMM). First, trajectory data enhancement is proposed to enrich the diversity of trajectory data, which improves the robustness of the transition model by allowing the model to learn more comprehensive and detailed information from the environment. Second, the computation burden has been significantly reduced by using effective RSS preprocessing, which reduces the data dimension and the solution domain. Finally, in order to increase the robustness and localization accuracy of the model, we propose a direction matching (DM) constraint to enhance the mapping relationship between the consecutive RSS signals and the one-step motion. Experiments show that ETMM has a better performance compared with the state-of-the-art method in terms of localization accuracy, computation cost, and robustness.

Digital surface model generation for drifting Arctic sea ice with low-textured surfaces based on drone images

Arctic sea ice is constantly moving and covered with low-textured surfaces, making it difficult to generate reliable digital surface models (DSMs) from drone images. The movement of sea ice makes georeferencing of DSMs difficult, and the low-textured surfaces of sea ice cause the uncertainty of image matching. This paper proposes a robust method to generate high-quality DSMs for drifting sea ice. To overcome the challenges, the proposed method introduces four improvements to the object-space-based image-matching pipeline: relative georeferencing to recover the horizontality and scale of sea-ice DSMs using a terrestrial light detection and ranging (LiDAR) dataset, match inspection to verify the matched points using several matching constraints, adaptive search-window adjustment to ensure distinct texture information through simple texture analysis, and robust vertical positioning to reduce the matching uncertainty via matching-indicator modeling. Performance evaluations were conducted with drone and LiDAR datasets obtained from a sea-ice campaign using the Korean Icebreaker Research Vessel (IBRV) Araon in the summer of 2017. The experimental results indicated that the proposed method can achieve significant quality enhancements compared with the existing matching method and that all the considerations contributed significantly to the enhancements.

Fault estimation for continuous‐time non‐linear switched systems with time‐varying delay based on intermediate estimator

In this work, the fault estimation (FE) problem is studied for continuous-time non-linear switched systems with time-varying delay over fading channel under average dwell time switching. A novel FE method based on the intermediate estimator is used to eliminate the observer matching constraint condition, where an intermediate variable is introduced to estimate the state and fault simultaneously. Meanwhile, the fading channel of data transmission is modelled as Bernoulli distributed white sequences. Furthermore, because the observer matching conditions are eliminated by the intermediate estimator, both the wirtinger integral inequality and an advanced reciprocal convex combination method are utilised to provide a tighter estimation on continuous derivative of certain Lyapunov–Krasovskii function over fading channel, leading to a less conservative result. Moreover, the solution of the parameters for intermediate estimator is characterised in the feasibility of linear matrix inequality problem. Finally, two numerical examples indicate the proposed technique is valid and superior.

Nonlinear filtering effects of intrawell matched stochastic resonance with barrier constrainted duffing system for ship radiated line signature extraction

Abstract Harmonic signatures extraction is of key interests in remote passive sonar detection and classification for its relevant to the inherent characteristics of engine and shaft/propeller rotations, of which the long-term challenging problem is to address the weak signatures from heavy noise background in the far field scenario. Signal filtering is often utilized as an essential preprocessing tool to purify the acquired noisy signals for applications, whereas stochastic resonance (SR) based nonlinear filter, has been proven effective for weak signature extraction especially under low signal-to-noise ratio (SNR) conditions. In this paper, a novel intrawell matched stochastic resonance (IMSR) is parameterized and implemented to further improve the output signal-to-noise ratio (SNR) with a barrier constrained quartic double-well potential. It can release the limitation on nonlinear system response, and ease the insufficient time-scale matching constraint and the inaccurate noise intensity estimation on potential parameters tuning. The nonlinear filtering effects are comparatively analyzed, which reflect superior performance and can break through the large sampling rate limitation as well. The distinct merits of IMSR are summarized as follows: 1) a superior low-noise output performance to the interwell response in the sense of nonlinear filtering effect; 2) a wider range of frequency response so that can ease the large sampling frequency limitation; 3) better anti-noise capability that expected to perform well in extracting weak line signatures from heavy background noise. Numerical analysis and application verification are performed to confirm the effectiveness and efficiency of the proposed method in comparison with a state-of-the-art second order matched stochastic resonance (SMSR) method, which reflect superior in different contexts.

Robust UAV Thermal Infrared Remote Sensing Images Stitching Via Overlap-Prior-Based Global Similarity Prior Model

The main problem of stitching unmanned aerial vehicle (UAV) thermal infrared remote sensing (TIRS) images lies in that the cumulative error caused by the inaccurate alignment of image matching pairs easily leads to deformation and even failure. Few studies in the literature are reported in stitching TIRS images. For the first time, we propose a simple and robust TIRS image stitching method by exploring prior information during flight. First, according to the position and orientation system information and parameter of camera, the overlap ratio of adjacent images is estimated, and the image pairs with high overlap ratio and high matching confidence in different directions are selected. Then, they are added into the alignment term of global similarity prior (GSP) model. Therefore, each image has more matching pairs constraints compared to the traditional construction method of matching pairs, which greatly improves the local registration capability of GSP and then prevents it from converging to the local optimal solution. The proposed method was extensively evaluated on a dataset including to 24 groups of large-scale farmland TIRS images collected in four experimental areas under different crop growth periods and meteorological conditions. Compared with two commercial tools and two representative stitching algorithms, the proposed method significantly improves the local alignment ability and overall stitching quality on both qualitative and quantitative evaluation. Besides, when the front overlap ratio is reduced from 85% to 70%, the proposed method still shows obvious advantages over the related methods and commercial tools, which improves the acquisition efficiency of UAV TIRS images.

Hierarchical stroke mesh: a new progressive matching method for detecting multi-scale road network changes using OpenStreetMap

Spatial feature matching is the key to detecting incremental changes in spatial data and extracting the updated information. The accuracy of spatial feature matching can depend on the structural organization of the data being compared; inconsistent data structures make comparison more difficult. OpenStreetMap (OSM) road network data, for example, is updated frequently to the point of being unstable, making the matching process used in information extraction susceptible to interference. To use OSM for comparison with other road data sources, this problem must be addressed. This paper proposes a new multi-scale dynamic matching algorithm based on a hierarchical stroke mesh (HSM) to detect matches between OSM data and professional surveying and mapping data and to update the change information. By improving the integrity and continuity of the stroke generation method and its algorithm for evaluating the importance of information, the algorithm proposed in this paper identifies the spatial hierarchy contained in the road network and abstracts the road network. The result is the HSM. The algorithm is based on multi-scale matching constraint rules designed from coarse to fine in terms of both resolution and granularity. It is used to detect one-to-one or one-to-many mapping relationships among different mesh levels (mesh, mesh boundary segment, and mesh inner segment). This allows progressive iterative matching between the older survey data and the newer OSM data. The results show that the HSM algorithm proposed in this paper can detect incremental changes between the two vector data sources quickly and accurately. Compared with others, this algorithm can effectively improve matching accuracy while sacrificing little performance.

An optimal monotone contention resolution scheme for bipartite matchings via a polyhedral viewpoint

Relaxation and rounding approaches became a standard and extremely versatile tool for constrained submodular function maximization. One of the most common rounding techniques in this context are contention resolution schemes. Such schemes round a fractional point by first rounding each coordinate independently, and then dropping some elements to reach a feasible set. Also the second step, where elements are dropped, is typically randomized. This leads to an additional source of randomization within the procedure, which can complicate the analysis. We suggest a different, polyhedral viewpoint to design contention resolution schemes, which avoids to deal explicitly with the randomization in the second step. This is achieved by focusing on the marginals of a dropping procedure. Apart from avoiding one source of randomization, our viewpoint allows for employing polyhedral techniques. Both can significantly simplify the construction and analysis of contention resolution schemes. We show how, through our framework, one can obtain an optimal monotone contention resolution scheme for bipartite matchings, which has a balancedness of 0.4762. So far, only very few results are known about optimality of monotone contention resolution schemes. Our contention resolution scheme for the bipartite case also improves the lower bound on the correlation gap for bipartite matchings. Furthermore, we derive a monotone contention resolution scheme for matchings that significantly improves over the previously best one. More precisely, we obtain a balancedness of 0.4326, improving on a prior 0.1997-balanced scheme. At the same time, our scheme implies that the currently best lower bound on the correlation gap for matchings is not tight. Our results lead to improved approximation factors for various constrained submodular function maximization problems over a combination of matching constraints with further constraints.

Performance analysis of 65 nm CMOS LNA using SSL technique for 5G cellular front-end receivers

This paper presents a performance analysis of a wideband low noise amplifier (LNA) that utilizes a 65 nm CMOS Samsung mm-wave process. The proposed CMOS LNA designed with new built-in techniques will overcome the challenges faced by device parasitic and electromagnetic (EM) losses. A suspended substrate line (SSL) is characterized and analyzed with its dual-band operation and achieves excellent EM compatibility. The traditional EM losses in bulk active and passive components have been incurred using built-in techniques to provide better linearity of LNA. The proposed mm-wave LNA enables it’s each family component to avoid leakage of EM waves and its interconnected parasitic losses in layout. An SSL based parallel-series network is optimized to achieve a wide bandwidth of 26 GHz to 34 GHz. The full design of LNA achieves the highest peak gain of 25 dB by using proper 50 Ω matching constraints over the wideband response of 27.8 GHz to 32.5 GHz. The fabricated chip of LNA is given a supply voltage of 1.2 V, and the calculated chip area is 0.35*0.22 mm2. The simulation and measurement results demonstrate the minimum noise figure of 2.5 dB and achieve the highest stability factor in the desired band of operation. The LNA also measured linearity with a 1 dB compression point where input power of −19dBm has obtained at 30.5 GHz.

Post‐filtering with surface orientation constraints for stereo dense image matching

AbstractDense image matching (DIM) is a critical technique when computing accurate 3D geometric information for many photogrammetric applications. Most DIM methods adopt first‐order regularisation priors for efficient matching, which often introduce stepped biases (also called fronto‐parallel biases) into the matching results. To remove these biases and compute more accurate matching results, this paper proposes a novel post‐filtering method by adjusting the surface orientation of each pixel in the matching process. The core algorithm formulates the post‐filtering as the optimisation of a global energy function with second‐order regularisation priors. A compromise solution of the energy function is computed by breaking the optimisation into a collection of sub‐optimisations of each pixel in a local adaptive window. The proposed method was compared with several state‐of‐the‐art post‐filtering methods on indoor, aerial and satellite datasets. The comparisons demonstrate that the proposed method obtains the highest post‐filtering accuracies on all datasets.

Single DDCC− based simulated floating inductors and their applications

Three simulated floating inductor (SFI) circuits containing a single DDCC− called as minus-type differential difference current conveyor are proposed. These SFIs are series lossy, parallel lossy and negative lossless ones. The used DDCC− in each of the proposed SFIs has a single Z terminal. Without needing any passive element matching constraints, each of the proposed SFIs is composed of a minimum number of passive elements. As application examples, second-order current-mode (CM) and voltage-mode low-pass (LP) filters derived from the proposed series lossy SFI are given. Also, a fourth-order CM Butterworth LP filter example is presented as another application. A second-order CM high-pass (HP) filter and an RLC resonant circuit obtained from the proposed parallel lossy SFI are given. All the simulations are achieved via SPICE program. Moreover, some experimental results for the proposed lossy SFIs and CM HP filter are given in order to show the performances.

Graph Matching Via the Lens of Supermodularity

Graph matching, the problem of aligning a pair of graphs so as to minimize their edge disagreements, has received widespread attention owing to its broad spectrum of applications in data science. As the problem is NP–hard in the worst-case, a variety of approximation algorithms have been proposed for obtaining high quality, suboptimal solutions. In this article, we approach the task of designing an efficient polynomial-time approximation algorithm for graph matching from a previously unconsidered perspective. Our key result is that graph matching can be formulated as maximizing a monotone, supermodular set function subject to matroid intersection constraints. We leverage this fact to apply a discrete optimization variant of the minorization-maximization algorithm which exploits supermodularity of the objective function to iteratively construct and maximize a sequence of global lower bounds on the objective. At each step, we solve a maximum weight matching problem in a bipartite graph. Differing from prior approaches, the algorithm exploits the combinatorial structure inherent in the problem to generate a sequence of iterates featuring monotonically non-decreasing objective value while always adhering to the combinatorial matching constraints. Experiments on real-world data demonstrate the empirical effectiveness of the algorithm relative to the prevailing state-of-the-art.

Blind Estimation of Central Blood Pressure Using Least-Squares with Mean Matching and Box Constraints.

Central aortic blood pressure (CABP) is a very-well recognized source of information to asses the cardiovascular system conditions. However, the clinical measurement protocol of this pulse wave is very intrusive and burdensome as it requires expert staff and complicated invasive settings. On the other hand, the measurement of peripheral blood pressure is much more straightforward and easy-to-get non-invasively. Several mathematical tools have been employed in the past few decades to reconstruct CABP waveforms from distorted peripheral pressure signals. More specifically, the cross-relation approach together with the widely used least-squares method, are shown to be effective as a way to estimate CABP waves. In this paper, we propose an improved cross-relation method that leverages the values of the diastolic and systolic pressures as box constraints. In addition, a mean-matching criterion is introduced to relax the need for the input and output mean values to be strictly equal. Using the proposed method, the root mean squared error is reduced by approximately 20% while the computational complexity is not significantly increased.