Efficient Matching Research Articles

User Matching on Blockchain for Computation Offloading in Ultra-Dense Wireless Networks

The popularity of mobile devices has driven the occurrence of ultra-dense wireless networks (UDNs) to accommodate high volumes of data therein generated by mobile users (MUs) running various mobile applications, such as virtual reality/augmented reality (VR/AR) and online gaming. Since many such mobile applications are computation-intensive, mobile edge computing (MEC), a promising computing paradigm that can provide on-demand computation resources (i.e., virtual machines (VMs)), is considered to adopt for allowing MUs to offload their computation tasks to EgSvrs in the proximity. Previous works have developed many schemes to schedule computation tasks to be offloaded to edge servers (EgSvrs). However, these developed schemes are centralized, which are vulnerable to congested coordination operations, and have no trustworthiness guarantee as well. In this paper, we propose to design a decentralized coordination scheme to orchestrate MUs and EgSvrs for scheduling computation tasks to their right VMs by taking advantage of blockchain technology. Specifically, we develop an efficient task-VM matching algorithm that jointly considers task execution time and energy consumption. Particularly, we prove the stability of the task-VM matching achieved by the developed matching algorithm. Besides, we further implement the developed task-VM matching algorithm on the blockchain by developing a smart matching contract to perform task-VM matching on the blockchain without trusted third parties. Extensive simulation results demonstrate that our decentralized coordination scheme can improve the performance significantly and converge to a stable state very quickly.

A Two-Port Multipolarization Rectenna With Orthogonal Hybrid Coupler for Simultaneous Wireless Information and Power Transfer (SWIPT)

A multipolarization rectenna, consisting of a two-port antenna fed by an orthogonal hybrid coupler and a medium-power rectifying circuit, is proposed. The orthogonal hybrid coupler has realized good isolation (S12 or S21 < −15 dB) between the two ports, which can be, respectively, connected to rectifying circuit and/or communication device for simultaneous wireless information and power transfer (SWIPT). Due to the symmetry of the coupler, the proposed rectenna can work at multipolarization modes, i.e., the circular polarization (CP) for the single-port operation and the linear polarization (LP) for the dual-port operation. Besides, a medium-power rectifying circuit is proposed, where its maximum radio frequency (RF) to direct current (dc) power conversion efficiencies is 53.6% (simulated) and 51.1% (experimental) at the input power of 0 and 0.5 dBm, respectively. By using the coupler, the conversion efficiency and impedance matching of the rectenna can be significantly improved. In addition, the power division between the two ports is considered at different modes. Compared with other rectennas, the proposed multifunctional rectenna has merits of multiple polarizations and multimode, which can therefore be used for the incoming waves in arbitrary polarization for SWIPT and cope with the uncertainty in real-world communication environments.

Distribution agnostic Bayesian matching pursuit based on the exponential embedded family

Compressed sensing (CS) is an emerging field that allows to recover high-dimensional sparse signal from a few compressed measurements. Classical CS algorithms using the Bayesian framework generally impose a sparseness-promoting prior on the signal, which may not characterize the real signals with diversity. Moreover, estimating the parameters involved in the prior is challenging especially for non-i.i.d. signals. In this paper, we propose an efficient prior-agnostic Bayesian matching pursuit for sparse signal recovery, which avoids the risk of imposing mismatched prior on the signals and enjoys lower complexity than Bayesian approaches due to the elimination of prior parameter estimation. Specifically, we utilize the noise model and the reduced exponential embedded family (EEF) to obtain an approximate likelihood of interest, and then find the most dominant supports with maximum approximate likelihoods in a greedy manner to give an approximate minimum mean squared error (MMSE) estimate for the sparse signal. Experiment results demonstrate that our method achieves lower normalized mean squared error (NMSE) while higher efficiency compared to the state-of-the-art methods.

Efficient History Matching of Thermally Induced Fractures Using Coupled Geomechanics and Reservoir Simulation

Waterflooding is a common recovery method used to maintain reservoir pressure and improve reservoir oil sweep efficiency. However, injecting cold water into a reservoir alters the state of in-situ formation stress and can result in the formation fracturing. In other words, it can cause the initiation and growth of thermally induced fractures (TIFs), even when the original fracture propagation pressure is not exceeded. TIFs can cause non-uniform distribution of the fluid flow in wellbores, a reduction in sweep efficiency, and early water breakthrough in nearby production wells. Modelling and history matching workflows that consider the dynamic nature of the TIF problem are critical. These workflows improve and validate reservoir and geomechanical models, identify and confirm observed TIF onset and propagation periods, and provide a history-matched sector model with the rock mechanical and thermal properties and stress gradients that can be used with confidence for subsequent studies. Modelling and the underlining assumptions of fluid flow in the TIF and reservoir matrix, as well as geomechanical changes due to cooling of the reservoir during injection, are detailed below. A 3D reservoir simulator coupled with 2D finite element TIF and geomechanical models were used to manually history match an injector (NI6) in the N Field sector reservoir model in which a TIF was observed. In this study, history matching workflows were developed to consider the dynamic nature of TIF development during waterflooding. The reservoir and geomechanical models were improved and validated via the observed TIF onset and propagation periods. The history-matched models produced can be used with confidence in subsequent studies. The practical workflows and guidelines developed here can be used in waterflooding operations during the modelling, design, and planning stages. The novelty of this study is the coupling approach of different complex processes done in order to capture dynamic changes during waterflooding operations. A similar history matching study could not be found in the literature.

Modeling and Analysis of E-Consults in Primary-Specialty Care Referrals

E-consults offer a digital platform where primary care physicians (PCPs) can consult specialists and obtain feedback or offer a direct specialty referral to their patients. The goal of this work is to investigate whether e-consults can advance the primary-specialty care interface and reduce referral delays. We provide a high-level abstraction of e-consult operations using an analytical framework that quantifies the benefit of e-consults in the context of efficient matching in a flexible service system. With heterogeneous patients who lack information regarding the severity of their condition, e-consults are conceptualized as Bayesian learning to estimate the true severity, parameterized by the level of e-consult efficacy, a measure of the degree of PCPs and specialists' communication and information sharing efforts. The analytical framework of games with incomplete information combines the Hotelling model, the Bayesian inference, and service science to fully capture the patient flow dynamics between the PCP and the specialist modulated by the revelation of hidden patient severity information enabled by e-consults. The fundamental e-consult economics is related to strategic patients who are oversensitive toward the service prices and congestion costs, which leads to the overuse of one type of service. Such a behavior can be mitigated by e-consults that facilitate shared diagnostic efforts that can navigate patients away from the more popular choice toward a more suitable one.

ESPM: Efficient Spatial Pattern Matching

With recent advances in information technologies such as global position system and mobile internet, a huge volume of spatio-textual objects have been generated from location-based services, which enable a wide range of spatial keyword queries. Recently, researchers have proposed a novel query, called Spatial Pattern Matching (SPM), which uses a pattern to capture the user's intention. It has been demonstrated to be fundamental and useful for many real applications. Despite its usefulness, the SPM problem is computationally intractable. Existing algorithms suffer from the low efficiency issue, especially on large scale datasets. To enhance the performance of SPM, in this paper we propose a novel Efficient Spatial Pattern Matching (ESPM) algorithm, which exploits the inverted linear quadtree index and computes matched node pairs and object pairs level by level in a top-down manner. In particular, it focuses on pruning unpromising nodes and node pairs at the high levels, resulting in a large number of unpromising objects and object pairs to be pruned before accessing them from disk. We experimentally evaluate the performance of ESPM on real large datasets. Our results show that ESPM is over one order of magnitude faster than the state-of-the-art algorithm, and also uses much less I/O cost.

A Novel Dielectric Slab Antenna Based on Microstrip-Franklin Excitation for mm-Waves

Abstract This work reports the development of a novel dielectric slab antenna based on microstrip-Franklin radiators, which enables efficient impedance matching among microstrip lines, substrate and air. The proposed antenna has been designed and numerically evaluated using ANSYS HFSS®. Its directivity ranges from 6.67 to 16.24 dB, as its length is increased from 19.9 to 170 mm. The 80 mm-length antenna prototype provides 11.14% fractional bandwidth (27.96 - 31.26 GHz) and 10.5 dBi gain at 30 GHz. The proposed antenna has been manufactured and applied to an indoor digital wireless communication link to provide 6 Gb/s throughput using 64-QAM modulation format, aiming 5G applications.

Research on Technical Characteristics of CDPF + SCR for China-III Heavy-Duty Diesel Vehicles Emission Retrofit Based on Remote Monitoring Method

In order to study the technology of reducing heavy-duty diesel vehicles PM and Nox emission, a China-III sprinkler driving on Tianjin expressway was selected to equip with CDPF+SCR for demonstration of PM and NOx emission retrofit. By using the remote monitoring method, the vehicle speed, engine speed, exhaust temperature and pressure drop of upstream and downstream CDPF, NOx concentration upstream and downstream SCR, geographical location and other data were collected and analyzed in real time, and analyze the characteristics of CDPF passive regeneration, SCR conversion efficiency and DPF matching with SCR. The results show that :the idling speed and the middle and low speed operating conditions of the sprinkler are evenly distributed, exhaust temperature at 175∼275 °C accounts for 74%, and CDPF started passive regenerative emission retrofit at 220°C, NOx concentration after installing SCR was mainly concentrated below 800ppm,the average NOx concentration was 620ppm, and the reduction rate of NOx concentration was 51.6%.

Efficient and Envy Minimal Matching

In priority-based matching problems, there is a trade-off between efficiency and reducing justified-envy. We show that finding an efficient and justified-envy minimal matching is an NP-hard problem when objects have weak priority rankings. Consequently, we focus on resolving the trade-off in a subclass of efficient mechanisms. First, we consider the class of efficient and strategyproof mechanisms and show that no group-strategyproof mechanism minimizes justified-envy in that class. Then, we restrict attention to the class of hierarchical exchanges and sequential dictatorship mechanisms, which are widely studied in the literature and applied in real-life resource allocation problems. We propose justified-envy minimal mechanisms for each of those classes.

Efficient Calibration of a Conceptual Hydrological Model Based on the Enhanced Gauss–Levenberg–Marquardt Procedure

Various models were developed in the past to simulate different hydrological processes. However, discrepancies between simulated and observed values are still significant and pose a challenge to many researchers. Models contain many parameters that cannot be directly measured. The values of most of these parameters are determined in the calibration process conditioning the efficiency of such models. This paper introduces the use of the enhanced Gauss–Levenberg–Marquardt (GLM) procedure in combination with the singular value decomposition (SVD) and Tikhonov regularization to improve the process of hydrological model calibration. The procedure is tested on a freely available hydrological model using a synthetic dataset. Based on several efficiency measures, the GLM procedure, in combination with SVD and Tikhonov regularization, was found to provide efficient model history matching and almost perfect parameter calibration. Moreover, by comparing the results of the proposed procedure with the results of global evolutionary calibration procedures, it was found that the only calibration using the combined GLM procedure gave a perfect fit in low flows. Last but not least, the noise in the calculation results with the combined GLM method was practically the same in either the calibration or validation procedure, suggesting that only computational noise remained in the results.

Sclera Recognition Based on Efficient Sclera Segmentation and Significant Vessel Matching

Abstract The visible blood vein in sclera has unique characteristics for each person, and it can be captured in visible light conditions. Therefore, the sclera vessels have been used as an important feature to improve the performance of identification recognition systems, for example, mobile payment and phone encryption system. But recent studies show that unsatisfactory sclera segmentation and tedious matching process are the two critical issues to degrade the performance of sclera identification system. In this paper, we propose a robust sclera recognition system with high accuracy and efficiency. First, a novel sclera segmentation method that provides adaptive threshold is proposed. Second, we have designed an improved vessels enhancement method based on the local grey distribution and local texture information, and least square linearization and moment invariants are utilized to extract features. Finally, an efficient matching strategy is put forward based on the detected significant vessels. The experimental results on one database with images captured in colour illumination prove that the proposed sclera recognition method has an obvious improvement regarding efficiency and accuracy over other sclera recognition systems.

A fully self-powered wearable monitoring system with systematically optimized flexible thermoelectric generator

Wearable electronics for personal healthcare and environment awareness are attracting more and more attentions and gaining wide market acceptance. Wearable monitoring system with continuous energy supply is necessary and still faces great challenge at present. Flexible thermoelectric generator (f-TEG) is competitive and promising energy solution due to its advantage of continuously harvesting energy from body heat under any condition. Up to now, energy efficiency of f-TEG at normal temperature is low and design of energy supply for wearable electronics is unthoughtful, which limit its practical applications. Here, we propose a systematic optimization method for designing f-TEG, which takes multi-objective optimization of power density, material consumption and power matching with wearable electronics into considerations. We optimize the number of thermoelectric grains, the fill factor and the series-parallel connection mode of f-TEG to achieve high energy efficiency and implement power matching with wearable sensory system. By this method, we develop a high-efficient f-TEG utilizing bismuth telluride grains assembled on flexible polyimide substrate. The f-TEG exhibits excellent power performance with power density of 3.5 µW/cm2 and 12.3 µW/g, and a boosted output voltage of 2.8–3.3 V at body temperature in motionless and windless condition. Furthermore, a self-powered wearable multisensory bracelet incorporating the f-TEG with multisensory system, tailor-designed intelligent power management and data display is developed, which allows continuously and simultaneously monitoring temperature, humidity, and activity of human body, entirely powered by human body heat. The f-TEG can be competent as a continuous and green energy supply for wearable monitoring system.

GrabTutor: A web and mobile application for tutor appointment system with GPS security feature

Background/Objectives: Academic tutoring is sought to aid students academically, keep them in school, and eventually earn a degree aside from the learning they get from a typical classroom setting. Academic institutions somehow start programs for peer tutorial sessions among students, but matching of tutees with tutors takes time especially for complex and complicated topics. It even brings a security concern about where students are conducting tutorials. Hence, this study aims to design a tutor appointment system with location tracking feature to give students a more efficient and safer tutorial experience. Methods: GrabTutor is composed of a mobile and web application that communicates through a cloud server. The tutees find tutors through the mobile application. The system generates a unique barcode for each tutorial appointment. A barcode scanner is embedded in the mobile application accessible to the tutor. Once the tutor scans, the system will trigger the timer and sends GPS coordinates to the server to track the location. The location will be represented by a marker on the map of the moderator\'s web application. Findings: Based on the results of the usability tests, the system easily matched tutees to the right tutors. The GPS monitoring system was proven to be a helpful tool to ease the risk of students having to go somewhere else to meet up with a tutor. Novelty/Applications: The proposed system enables quick and efficient matching of tutors to tutees and integrates GPS information of students within the campus during the conduct of the tutorial to track their whereabouts for a more secure tutorial experience. Keywords: Tutor appointment; location tracking; barcode; GPS; security

Stable Improvement Cycles in a Controlled School Choice

We consider a stable improvement problem in a controlled school choice model which covers those of several previous studies. First, we consider the case where the priority for each school is a weak order. We derive a sufficient condition for a stable matching to be constrained efficient. It is also a necessary condition, and utilizing this property, we provide a class of algorithms each of which, once a stable matching is obtained, derives a constrained efficient matching that Pareto dominates the initial matching. We further allow that the inter-type priorities are not weak but just partial orders. Although the sufficient part holds, the necessity part does not, and there remains a case where our algorithms cannot derive any constrained efficient matching.

Binocular Camera Calibration for Underwater Stereo Matching

This paper presents an efficient stereo matching method applied to underwater with a binocular camera. Since underwater calibration is an inconvenient task and the traditional imaging model in the air and the parameters of the camera are no longer applicable to the underwater because of the distortion caused by light refraction. To solve this problem, we proposed a way to calibrate the binocular camera in the air for underwater stereo matching. We studied the imaging principle of the camera, and derived the relationship of the camera between in the air and underwater. Then, we performed underwater stereo matching experiments using the parameters of the camera calibrated in the air and found it to be effective.

Parameter selection framework for stereo correspondence

In this paper, we propose a method to select parameter values for stereo matching methods. The proposed method was trained in a supervised manner, and an evolutionary algorithm is used to select optimized parameter values for a given domain and a cost function constructed to measure the goodness level of candidate parameter values. Performance of the proposed method is compared to that of five current stereo matching methods, including the efficient large-scale stereo matching, belief propagation, semi-global block matching, stereo matching by training a convolutional neural network to compare image patches, and the efficient deep learning for stereo matching, for KITTI 2012, KITTI 2015, Middlebury, and EISAT datasets. The optimized parameters improve accuracy for all stereo matching methods considered, with some cases of improvement reaching up to $$24\%$$. Source code and experimental results are available online.

BEBLID: Boosted efficient binary local image descriptor

Efficient matching of local image features is a fundamental task in many computer vision applications. However, the real-time performance of top matching algorithms is compromised in computationally limited devices, such as mobile phones or drones, due to the simplicity of their hardware and their finite energy supply. In this paper we introduce BEBLID, an efficient learned binary image descriptor. It improves our previous real-valued descriptor, BELID, making it both more efficient for matching and more accurate. To this end we use AdaBoost with an improved weak-learner training scheme that produces better local descriptions. Further, we binarize our descriptor by forcing all weak-learners to have the same weight in the strong learner combination and train it in an unbalanced data set to address the asymmetries arising in matching and retrieval tasks. In our experiments BEBLID achieves an accuracy close to SIFT and better computational efficiency than ORB, the fastest algorithm in the literature.

Real-Time Route Search by Locations

With the proliferation of GPS-based data (e.g., routes and trajectories), it is of great importance to enable the functionality of real-time route search and recommendations. We define and study a novel Continuous Route-Search-by-Location (C-RSL) problem to enable real-time route search by locations for a large number of users over route data streams. Given a set of C-RSL queries where each query q contains a set of places q.O to visit and a threshold q.θ, we continuously feed each query q with routes that has similarity to q.O no less than q.θ. We also extend our proposal to support top-k C-RSL problem where each query continuously maintains k most similar routes. The C-RSL problem targets a variety of applications, including real-time route planning, ridesharing, and other location-based services that have real-time demand. To enable efficient route matching on a large number of C-RSL queries, we develop novel parallel route matching algorithms with good time complexity. Extensive experiments with real data offer insight into the performance of our algorithms, indicating that our proposal is capable of achieving high efficiency and scalability.

Pay Your Trip for Traffic Congestion: Dynamic Pricing in Traffic-Aware Road Networks

Pricing is essential in optimizing transportation resource allocation. Congestion pricing is widely used to reduce urban traffic congestion. We propose and investigate a novel Dynamic Pricing Strategy (DPS) to price travelers' trips in intelligent transportation platforms (e.g., DiDi, Lyft, Uber). The trips are charged according to their “congestion contributions” to global urban traffic systems. The dynamic pricing strategy retrieves a matching between n travelers' trips and the potential travel routes (each trip has k potential routes) to minimize the global traffic congestion. We believe that DPS holds the potential to benefit society and the environment, such as reducing traffic congestion and enabling smarter and greener transportation. The DPS problem is challenging due to its high computation complexity (there exist kn matching possibilities). We develop an efficient and effective approximate matching algorithm based on local search, as well as pruning techniques to further enhance the matching efficiency. The accuracy and efficiency of the dynamic pricing strategy are verified by extensive experiments on real datasets.

Multilateralism and international governmental organizations: principles and instruments

PurposeThe purpose of this paper is to build a conceptual framework helping to understand the relationship between the principle of multilateralism in global policy relations and the role of international governmental organizations (IGOs). The paper develops conceptual building blocks to design adequate for international organizations for result-oriented effective multilateralism.Design/methodology/approachLiterature review and in-depth theoretical analysis served to construct a framework that helps to see the variety of IGOs against global policy problems they serve to solve.FindingsThe analysis highlighted several lessons that can be learned, including the need for an efficient match between the nature of the global policy problem and the mandate and the size of IGO. The paper indicated the importance of efficient mechanisms to make international organizations accountable to member states while allowing for effective leadership.Research limitations/implicationsThe heterogeneity of IGOs makes a generalization difficult, hence, the proposed framework necessarily remains relatively generic. Still, this paper provides a first analytical basis for the comparison of IGO with regard to global policy problems they have been set to solve.Practical implicationsThe author pointed to the way how to improve the fit between global policy problems and IGOs to make multilateralism more effective. The framework can be used to learn what can be expected from a given IGO and to see IGOs in their proper roles.Social implicationsThe paper might of interest to decision-makers and international public opinion eager to either criticize or praise international organizations based on a simplistic, if not ideological approaches. The paper develops arguments that help influence the evolution of international organizations.Originality/valueIGO are increasing in number and forms leading to confusion to their role and impact. This framework can be used to realistically assess the role of IGOs in global public policies.