Matching Efficiency Research Articles

Graphene-based plasmonic nanoantenna with trapezoidal structure in THz band

Mainly manipulating emitted waves via plasmonic resonance is a fundamental characteristic of optical antennas. Plasmonic nanoantennas are used for terahertz (THz) radiation caused by optical interaction with surface resonance. The designed nanoantenna is such that the plasmonic resonance of the nanoantenna changes by controlling the graphene chemical potential (μc) from 0 eV to 0.2 eV. Although a dielectric layer separates the trapezoidal graphene sheets, an essential difference in the scattering parameters and the radiation pattern is observed since an external direct current bias changed the graphene surface impedance to about 860 Ω, which is the optimal value for reducing return loss in nano-systems. In other words, the plasmonic resonance frequencies can be tuned by the effect of a localised electrostatic field. Also, the trapezoidal graphene-based plasmonic nanoantenna has a high quality factor (Q-factor) from 2.35 to 5.71. Therefore, dynamic control, enhancing the input impedance and stable efficiency of graphene-based plasmonic nanoantennas, improves performance and matching at THz nano-systems for many applications of optoelectronic devices.

An Improved BLG Tree for Trajectory Compression with Constraints of Road Networks

With the rising popularity of portable mobile positioning equipment, the volume of mobile trajectory data is increasing. Therefore, trajectory data compression has become an important basis for trajectory data processing, analysis, and mining. According to the literature, it is difficult with trajectory compression methods to balance compression accuracy and efficiency. Among these methods, the one based on spatiotemporal characteristics has low compression accuracy due to its failure to consider the relationship with the road network, while the one based on map matching has low compression efficiency because of the low efficiency of the original method. Therefore, this paper proposes a trajectory segmentation and ranking compression (TSRC) method based on the road network to improve trajectory compression precision and efficiency. The TSRC method first extracts feature points of a trajectory based on road network structural characteristics, splits the trajectory at the feature points, ranks the trajectory points of segmented sub-trajectories based on a binary line generalization (BLG) tree, and finally merges queuing feature points and sub-trajectory points and compresses trajectories. The TSRC method is verified on two taxi trajectory datasets with different levels of sampling frequency. Compared with the classic spatiotemporal compression method, the TSRC method has higher accuracy under different compression degrees and higher overall efficiency. Moreover, when the two methods are combined with the map-matching method, the TSRC method not only has higher accuracy but also can improve the efficiency of map matching.

Legal aspects of artificial intelligence in the employment process

The introduction of artificial intelligence in all domains of life is the most transformative process in recent history. It is also a highly dynamic process, and due to the pace of technological development, a very limited legal framework is available to address issues of human rights, ethics, transparency, privacy, safety and accountability. During the last few years, artificial intelligence started to reshape employment processes. Positive aspects of the introduction of AI in the employment process are efficiency and quality in job matching, digitalisation and acceleration of the process, ability to process large data and match job seekers to available vacancy announcements, the alleviation of administrative burdens of employees of employment agencies and giving them strategic and innovative roles. All these are indispensable in present times when demographic challenges in European countries are leading to increased labour migrations and require changes in the recruitment process. The paper explores the current challenges of AI, i.e. how to achieve human-centred values and fairness of AI use during the employment process, preventing algorithmic bias and discriminatory application of AI tools. In order to harness the maximum benefits of AI, we need to develop a regulatory framework that would be enforceable, inclusive and adaptive (OECD), particularly knowing that most AI solutions are privately owned and developed for commercial purposes.

On-Chip Integration of a Plasmonic FET Source and a Nano-Patch Antenna for Efficient Terahertz Wave Radiation.

Graphene-based Field-Effect Transistors (FETs) integrated with microstrip patch antennas offer a promising approach for terahertz signal radiation. In this study, a dual-stage simulation methodology is employed to comprehensively investigate the device's performance. The initial stage, executed in MATLAB, delves into charge transport dynamics within a FET under asymmetric boundary conditions, employing hydrodynamic equations for electron transport in the graphene channel. Electromagnetic field interactions are modeled via Finite-Difference Time-Domain (FDTD) techniques. The second stage, conducted in COMSOL Multiphysics, focuses on the microstrip patch antenna's radiative characteristics. Notably, analysis of the S11 curve reveals minimal reflections at the FET's resonant frequency of 1.34672 THz, indicating efficient impedance matching. Examination of the radiation pattern demonstrates the antenna's favorable directional properties. This research underscores the potential of graphene-based FETs for terahertz applications, offering tunable impedance matching and high radiation efficiency for future terahertz devices.

RF Packaging and Design for Development of High Performance 5G mmWave Modules

5G sub-6GHz networks have already been deployed in many regions around the world. However, the deployment of 5G mmWave networks is yet to meet expectations. This is partly because of the challenges associated with hardware development of 5G mmWave systems. One example of such a challenge is to overcome the severe degradation of signal-to-noise (SNR) ratio due to intra- and inter-system signal losses at mmWave frequencies. This deteriorates the signal quality, limits transmission distances and may lead to unreliable mobile communication. In this work, we present RF packaging platforms and technologies as well as an approach for scalability of 5G RF frontend integrated circuits (ICs) and antennas which overcome intra- and inter-system losses. This leads to the development of high-performance and scalable 5G MIMO (multiple input multiple output) modules for reliable mobile communication at mmWave frequencies. The platforms provide very short and low-loss signal paths with few geometrical discontinuities between the frontend ICs and antennas. This leads to significant reduction in signal reflections and attenuation, thus improving the matching efficiency of the antennas and the equivalent isotropic radiated power (EIRP) of the entire system. The platforms also enable antennas to be integrated on layers directly above the ICs, and thereby ensures scalability of the ICs and antennas in both lateral dimensions for 5G mmWave massive MIMO. To demonstrate the functionality of our proposed packaging platforms, technologies and antennas, we performed in-depth RF designs, measurements and characterization of packaging materials, technologies and platforms as well as antennas for 5G mmWave applications.

An Ultrahigh‐Rectification‐Ratio WSe2 Homojunction Defined by High‐Efficiency Charge Trapping Effect

AbstractAlthough 2D material van der Waals heterostructures (vdWHs) exhibit many novel properties and applications, 2D homojunctions have unique advantages in interface lattice matching, band continuity, and charge transfer efficiency. However, the rectification performances of 2D homojunction diodes are severely limited by the small junction barrier, mainly due to inefficient charge doping. In this work, an ultrahigh‐rectification‐ratio WSe2 homojunction diode achieved by the semi‐floating gate doping of graphdiyne oxide (GDYO) is reported. Utilizing the WSe2/GDYO direct charge trapping mode can free the inhibition of charge capturing efficiency by removing conventional insulating barriers and thus improve the rectification ratio. The C─C(sp) and oxygen‐containing functional groups in the GDYO layer can provide outstanding charge‐trapping ability due to their unsaturation. Furthermore, the oxygen plasma treatment used for oxidizing graphdiyne (GDY) into GDYO can make GDYO a flatter surface, thus creating strong‐coupling WSe2/GDYO interfaces to improve the charge transfer efficiency and enhance the electrostatic doping effect. Besides, the WSe2/GDYO interfaces are confirmed to possess a higher junction barrier than that of the WSe2/GDY interfaces. This research proposes a brand‐new approach to building p–n junctions via charge trapping and the homojunction diode with a record‐highest rectification ratio of up to 106 is obtained.

A multi-sensor fusion framework with tight coupling for precise positioning and optimization

In the dynamic landscape of artificial intelligence and robotics, the pursuit of accurate positioning in mobile robots has intensified. This research addresses the limitations of single-sensor SLAM (Simultaneous Localization and Mapping) techniques in complex settings by harnessing the collective strengths of LiDAR (Light Detection And Ranging), Camera, IMU (Inertial Measurement Unit), and GNSS (Global Navigation Satellite System) sensors. The proposed multi-sensor tightly-coupled SLAM framework is an integration of point-line feature-based laser–visual–inertial odometry, visual–laser fusion loop closure detection, and factor graph-based back-end optimization. Within the visual–inertial subsystem, an advanced LSD (Line Segment Detector) feature extraction strategy is introduced, incorporating point-line fusion to enhance visual line features. Additionally, the laser point cloud is projected onto the camera coordinate system, establishing depth associations with visual attributes. Strengthening the robustness of the visual–inertial subsystem in low-texture environments, camera poses undergo optimization through a sliding-window bundle adjustment method. In the laser-inertial subsystem, IMU preintegration mitigates laser point cloud distortion. Extracting edge and plane features, coupled with frame-to-local-map matching, enhances matching efficiency while streamlining computational intricacies. This amalgamation forms the basis of the laser–visual–inertial odometry fusion system. To overcome the limitations of standalone visual and laser-based loop closure detection, a dual-loop closure method utilizing visual–laser fusion is proposed. Leveraging the DBoW2 bag-of-words model, complemented by temporal–spatial consistency checks, enhances detection efficiency and accuracy. The integration of GNSS factors imparts global constraints for expansive outdoor scenarios. Employing factor graph-based back-end optimization, the refinement of laser–visual–inertial odometry factors, visual–inertial odometry factors, IMU preintegration factors, loop closure factors, and GNSS factors culminates in precise global pose estimation and high-fidelity point cloud maps. Through rigorous evaluation of the M2DGR dataset and a mobile robot platform, the proposed methodology emerges as an exemplar of performance, showcasing superiority over the state-of-the-art LIO-SAM technique. Achieving a reduction of 2.86m and 3.23m in the root mean square error of absolute pose estimation across divergent environments, this approach exhibits remarkable efficacy in outdoor scenarios, thereby elevating the precision and resilience of SLAM algorithms for mobile robots.

A Study on the Matching Efficiency of Malaysia's Labor Market during the Covid-19 Pandemic using the DEA-Malmquist Productivity Index

This paper analyses the matching efficiency of the Malaysian labor market during the COVID-19 pandemic. Employing Malmquist DEA, the results indicate that from January 2020 to December 2021, the average total factor productivity change is 0.967. TFPCH declined and had an average growth rate of -3.3% during the sample period. Decomposing TFPCH into technical change (TECHCH) and efficiency change (EFFCH), low TECHCH, with an average growth rate of -10.2%, is the main reason why TFPCH is far from the effective frontier. The TFPCH of the mining and quarrying industry is the highest, with an average growth of 18.7%. The TFPCH of both manufacturing and construction industries are the lowest with average annual growths of -1.7% and -7.6% respectively. Overall, it can be concluded that one of the main drivers to enhance the matching efficiency of the labor market is technical change or technological progress. One way is to strengthen online platforms that can help the firm or industry to match job vacancies to job seekers, even during economic shocks.

Dual Band 1x4 Linear Metamaterial Bowtie Antenna Array for Autonomous Vehicle in Public Safety Band Communications

This research presents the design, simulation, and fabrication of a dual-band 1x4 linear metamaterial bowtie antenna array for applications in 5G wireless systems and public safety band communications. The single-element antenna is a compact dual-band structure with a complementary split-ring resonator (TCSRR) element, and it exhibits resonant frequencies of 3.5 GHz and 4.9 GHz, covering the sub-6GHz 5G spectrum and the public safety band. The antenna aray design is optimized for impedance matching and radiation efficiency. The 1x4 linear antenna array is designed with a quarter-wave transformer feed network and carefully controlled mutual coupling to enhance gain and directionality. Simulated and measured results confirm the performance of the array, with a reflection coefficient within -10 dB from 3.2 GHz to 3.85 GHz and from 4.55 GHz to 5.95 GHz and 3.45 GHz to 3.75 GHz and 4.45 GHz to 5.7 GHz, respectively. The array exhibits a peak realized gain of up to 8.7 dBi and efficient radiation patterns. This work offers promising insights into the development of antenna systems for advanced wireless communication applications and vehicle-to-vehicle communication in public safety band.

Deep Learning Perspectives on Efficient Image Matching in Natural Image Databases

With the proliferation of digital content, efficient image matching in natural image databases has become paramount. Traditional image matching techniques, while effective to a certain extent, face challenges in dealing with the high variability inherent in natural images. This research delves into the application of deep learning models, particularly Convolutional Neural Networks (CNNs), Siamese Networks, and Triplet Networks, to address these challenges. We introduce various techniques to enhance efficiency, such as data augmentation, transfer learning, dimensionality reduction, efficient sampling, and the amalgamation of traditional computer vision strategies with deep learning. Our experimental results, garnered from specific dataset, demonstrate significant improvements in image matching efficiency, as quantified by metrics like precision, recall, F1-Score, and matching time. The findings underscore the potential of deep learning as a transformative tool for natural image database matching, setting the stage for further research and optimization in this domain.

A Robust Localization Approach Based on Point Cloud Descriptor and SLAM in Urban Environment

The importance of autonomous driving localization to autonomous driving systems cannot be overstated, reliable and high-precision localization is the focus of research in autonomous driving. Many studies at this stage use multiple sensors to generate high-definition maps for matching and localization. The labor and economic costs required to maintain such maps are often high. This paper proposes a fusion localization framework for simultaneous localization and mapping (SLAM) and matching localization. It could afford high-precision localization results of autonomous vehicles using only light detection and ranging (LiDAR) sensors. Concretely, a novel global point cloud descriptor, named binary scan context (BSC) is proposed, for matching localization. It encodes the structural features in the z-height direction in the point cloud space in a binary way. An efficient two-stage matching strategy is proposed to improve the efficiency of matching. Experiments on public datasets and vehicles have demonstrated the validation and precision of the method.

Medicine-Shelf matching strategy based on Bayesian convolutional neural network with fuzzy analytic hierarchy process

In pharmaceutical warehousing operations, a scientific and reasonable medicine and shelf matching strategy can improve medicine shelving efficiency and manual work efficiency. However, the traditional matching strategy has the problems of static matching and low matching efficiency, so this paper proposes the matching algorithm for drugs and shelves based on the fuzzy analytic hierarchy process and Bayesian convolutional neural network (FAHP-BCNN). First, we propose a drug-shelf matching degree model and discuss the influence of the dynamic matching process on the matching results by studying two parts: the attribute matching degree and the dynamic matching influence degree. Secondly, this paper quantitatively assessed the importance of the two matches by means of the fuzzy analytic hierarchy process. Finally, we mapped the massive matching information onto Bayesian convolutional neural network nodes, constructed a dynamic matching network model, and obtained competitive matching strategies and results. The experimental results show that the FAHP-BCNN algorithm exhibits better matching results in all three scenarios with different quantity ratios of drugs and shelf matching. Compared with the fixed cargo matching strategy of the ABC classification method, the FAHP-BCNN algorithm shows excellent performance in both the manual walking distance index and fatigue index. In summary, the drug-shelf matching algorithm based on FAHP-BCNN is effective and can provide theoretical support for pharmaceutical warehousing enterprises to perform drug-shelf matching.

A Branch-and-Price Algorithm Enhanced by Decision Diagrams for the Kidney Exchange Problem

Problem definition: Kidney paired donation programs allow patients registered with an incompatible donor to receive a suitable kidney from another donor, as long as the latter’s co-registered patient, if any, also receives a kidney from a different donor. The kidney exchange problem (KEP) aims to find an optimal collection of kidney exchanges taking the form of cycles and chains. Methodology/results: We develop the first decomposition method that is able to consider long cycles and long chains for projected large realistic instances. Particularly, we propose a branch-and-price framework in which the pricing problems are solved (for the first time in packing problems in a digraph) through multivalued decision diagrams. We present a new upper bound on the optimal value of the KEP, obtained via our master problem. Computational experiments show superior performance of our method over the state of the art by optimally solving almost all instances in the PrefLib library for multiple cycle and chain lengths. Managerial implications: Our algorithm also allows the prioritization of the solution composition, for example, chains over cycles or vice versa, and we conclude, similar to previous findings, that chains benefit the overall matching efficiency and highly sensitized patients. Funding: This work was supported by NSERC Discovery Grant (RGPIN-2021-02609). Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.0192 .

Search Frictions in Many‐to‐one Markets*

This article studies how search frictions affect competition and matching efficiency in many‐to‐one loan markets where a borrower requires support from multiple investors and coordination is desired but absent. We develop a dynamic search model and show that borrowers employ mixed strategies in quoting interest rates. More importantly, we find that in many‐to‐one markets, the rate dispersion caused by search frictions facilitates coordination and hence improves allocation efficiency relative to a no friction environment. Further, we empirically present stylized facts consistent with the theoretical predictions and structurally estimate the impact of search frictions on matching efficiency.

PSiamRML: Target Recognition and Matching Integrated Localization Algorithm Based on Pseudo-Siamese Network

The positioning function of unmanned aerial vehicles (UAVs) is a challenging and fundamental research topic and is the premise for UAVs to realize autonomous navigation. The disappearance of satellite signals makes it challenging to achieve accurate positioning. Thus, visual positioning algorithms based on computer vision have been proposed in recent years and these algorithms have produced good results. However, these algorithms have relatively simple functions and cannot perceive the environment. Their versatility is poor, and mismatching often occurs, which affects the positioning accuracy. Aiming to address the need for integrated target recognition, target matching, and positioning of UAVs, we propose an algorithm that integrates the target recognition, matching, and positioning functions by combining the single-shot multibox detector (SSD) algorithm with the deep feature matching algorithm. This algorithm is based on the idea of pseudo-Siamese networks and the SSD algorithm, introducing a deep feature matching method to directly calculate the correspondence between two images. The main idea is to use the VGG network trained by the SSD target recognition algorithm to extract deep features, without any special training for feature matching. Finally, by sharing neural network weights, the integrated design of target recognition and image-matching localization algorithms is achieved. Mismatches between the real-time and reference images are addressed by introducing the grid-based motion statistics algorithm to optimize the matching result and improve the correct matching efficiency of the target. The University-Release dataset was used to compare and analyze the performance of the proposed algorithm to verify its superiority and feasibility. The results show that the matching accuracy of the PSiamRML algorithm is generally good and that it significantly compensates for changes in the contrast, scale, brightness, blur, deformation, and so on, apart from improving the stability and robustness. Finally, a matching test scenario with aerial images captured by an S1000 six-rotor UAV served to verify the effectiveness and practicability of the PSiamRML algorithm.

MOFs derived multi-scale lamellar network for broadband electromagnetic wave absorber

Developing an efficient EM wave absorbing material that can broadband absorb EM waves in a wide thickness range is a significant challenge. Herein, we report a multi-scale heterogeneous Co/C composite foam with a three-dimensional (3D) lamellar network to address this challenge. The internal structure of melamine foam (MF) is engineered via carboxymethyl cellulose (CMC) in-situ lamella formation to guarantee excellent impedance matching. Meanwhile, the CMC lamellar network extends the EM wave transmission path. The MOF-derived particles grow uniformly in the lamellar network, covering channels of EM wave transmission, which significantly enriches the EM wave loss mechanism and enhances attenuation capability. Benefiting from the synergistic effect of the 3D lamellar network and multi-scale heterogeneous carbon source, an EM absorber strikes a balance between impedance matching and absorption efficiency. As a result, Co/C@MC delivers an effective absorption bandwidth (EAB) that is more than 8 GHz in the whole range of thicknesses from 3.5 to 8 mm, Ni/C@MC exhibits an EAB of 8.5 GHz at 3.3 mm and Fe/C@MC shows the optimal reflection loss of −76.9 dB at 2.5 mm. This work provides a lamellar network/regional enhancement unit strategy for preparing ultra-efficient broadband EM wave absorbing materials.

Equity and Efficiency in Dynamic Matching: Extreme Waitlist Policies

Waitlists are commonly used to allocate scarce resources, such as public housing or organs. Waitlist policies attempt to prioritize agents who wait longer by assigning them priority points (à la first come, first served). We show that such point systems can lead to severe inequality across the agents’ assignment probabilities unless they use randomization. In particular, deterministic point systems lead to a more unequal allocation than any other rule that prioritizes earlier arrivals, an axiom that ensures that agents who wait longer are treated (weakly) better. Among the policies abiding by this axiom, we show that service in random order (SIRO) leads to the most equal allocation. From a utilitarian perspective, we show that the planner faces no trade-off between equity and efficiency when the flow utility from waiting is nonnegative or negative and increasing over time. In these cases, SIRO is also the most efficient policy. However, when the flow cost of waiting increases over time, then the planner may face an efficiency–equity trade-off: SIRO remains the most equitable policy but may not be the most efficient one. This paper was accepted by Omar Besbes, revenue management and market analytics.

A Path Increment Map Matching Method for High-Frequency Trajectory

Aiming at the problems of low matching accuracy and slow matching speed of high-frequency trajectory data in complex urban road networks, this paper proposes a matching method based on path increment. This method consists of two parts: combined filtering and incremental matching. Firstly, the road network is simplified through combined filtering, and then the incremental matching is carried out by taking the paths as increments. In the matching procedure, a comprehensive evaluation scheme of similarity based on distance factor and curvature is adopted. The above measures effectively reduce the impact of complex road segments on the matching results, while the path increment method enables the matching process to be executed more rapidly and accurately. The experiments were conducted using the Geolife datasets. The results show that our algorithm has obvious advantages over similar algorithms in terms of matching accuracy and efficiency, and shows good stability in road matching tests with different complexity.

Matching Mechanisms for Refugee Resettlement

Current refugee resettlement processes account for neither the preferences of refugees nor the priorities of hosting communities. We introduce a new framework for matching with multidimensional knapsack constraints that captures the (possibly multidimensional) sizes of refugee families and the capacities of communities. We propose four refugee resettlement mechanisms and two solution concepts that can be used in refugee resettlement matching under various institutional and informational constraints. Our theoretical results and simulations using refugee resettlement data suggest that preference-based matching mechanisms can improve match efficiency, respect priorities of communities, and incentivize refugees to report where they would prefer to settle. (JEL C78, D82, J15, J18)

FILNet: Fast Image-Based Indoor Localization Using an Anchor Control Network.

This paper designs a fast image-based indoor localization method based on an anchor control network (FILNet) to improve localization accuracy and shorten the duration of feature matching. Particularly, two stages are developed for the proposed algorithm. The offline stage is to construct an anchor feature fingerprint database based on the concept of an anchor control network. This introduces detailed surveys to infer anchor features according to the information of control anchors using the visual-inertial odometry (VIO) based on Google ARcore. In addition, an affine invariance enhancement algorithm based on feature multi-angle screening and supplementation is developed to solve the image perspective transformation problem and complete the feature fingerprint database construction. In the online stage, a fast spatial indexing approach is adopted to improve the feature matching speed by searching for active anchors and matching only anchor features around the active anchors. Further, to improve the correct matching rate, a homography matrix filter model is used to verify the correctness of feature matching, and the correct matching points are selected. Extensive experiments in real-world scenarios are performed to evaluate the proposed FILNet. The experimental results show that in terms of affine invariance, compared with the initial local features, FILNet significantly improves the recall of feature matching from 26% to 57% when the angular deviation is less than 60 degrees. In the image feature matching stage, compared with the initial K-D tree algorithm, FILNet significantly improves the efficiency of feature matching, and the average time of the test image dataset is reduced from 30.3 ms to 12.7 ms. In terms of localization accuracy, compared with the benchmark method based on image localization, FILNet significantly improves the localization accuracy, and the percentage of images with a localization error of less than 0.1m increases from 31.61% to 55.89%.