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The dynamic parameters of multiple projectiles that are fired using multi-barrel weapons in high-frequency continuous firing modes are important indicators to measure the performance of these weapons. The characteristics of multiple projectiles are high randomness and large numbers launched in a short period of time, making it very difficult to obtain the real dispersion parameters of the projectiles due to the occlusion or coincidence of multiple projectiles. Using six intersecting-screen testing system, in this paper, we propose an association recognition and matching algorithm of multiple projectiles using a temporal and spatial information constraint mechanism. We extract the output signal from each detection screen and then use the wavelet transform to process the output signal. We present a method to identify and extract the time values on which the projectiles pass through the detection screens using the wavelet transform modulus maximum theory. We then use the correlation of the output signals of three parallel detection screens to establish a correlation coefficient recognition constraint function for the multiple projectiles. Based on the premise of linear projectile motion, we establish a temporal and spatial constraint matching model using the projectile's position coordinates in each detection screen and the projectile's time constraints within the multiple intersecting-screen geometry. We then determine the time values of the multiple projectiles in each detection screen using an iterative search cycle registration, and finally obtain the flight parameters for the multiple projectiles in the presence of uncertainty. The proposed method and algorithm were verified experimentally and can solve the problem of uncertainty in projectiles flight parameter under different multiple projectile firing states.

This paper addresses the problem of reconstructing 3D poses of multiple people from a few calibrated camera views. The main challenge of this problem is to find the cross-view correspondences among noisy and incomplete 2D pose predictions. Most previous methods address this challenge by directly reasoning in 3D using a pictorial structure model, which is inefficient due to the huge state space. We propose a fast and robust approach to solve this problem. Our key idea is to use a multi-way matching algorithm to cluster the detected 2D poses in all views. Each resulting cluster encodes 2D poses of the same person across different views and consistent correspondences across the keypoints, from which the 3D pose of each person can be effectively inferred. The proposed convex optimization based multi-way matching algorithm is efficient and robust against missing and false detections, without knowing the number of people in the scene. Moreover, we propose to combine geometric and appearance cues for cross-view matching. Finally, an efficient tracking method is proposed to track the detected 3D poses across the multi-view video. The proposed approach achieves the state-of-the-art performance on the Campus and Shelf datasets, while being efficient for real-time applications.

Network virtualization techniques are promising to overcome the obstacle of applying and expanding costly traditional networks in the Industrial Internet of Things (IIoT). Artificial Intelligence (AI)-enhanced distributed resource management in edge networks has aroused researcher widespread attention. However, dynamically arrived service requests and limited edge resources complicate the service scheduling issue. In this paper, we establish a dynamic network virtualization technique enabled Service Function Chain (SFC) orchestration framework in IIoT, formulate the joint optimization problem to maximize total utility and decompose it into two sub-problems, i.e., SFC selection and dynamic SFC orchestration. A Dynamic Orchestration of SFC (DOS) scheme, consisting of resourceaware matching algorithm and Averaged Multi-step Double Deep q-network (AMD2) algorithm, is designed to embed SFC requests distributedly on the optimal VNF chains. At last, we validate the superiority of our proposed DOS scheme by experimental results.

The accuracy and stability of navigation algorithms are crucial preconditions for underwater gravity matching aided navigation. To improve the matching accuracy and robustness of the matching algorithm, this paper presents a novel gravity matching navigation algorithm based on multiscale search and Hadamard transformed difference. The Hadamard transformation process was first time introduced in the new algorithm for the Walsh–Hadamard kernel function with the property of energy conservation. The gravity measurement sequences could be converted to the Hadamard domain; thus, the difference in numerical values, tendency, and spatial structure of the gravity measurement sequence were also a focus in the new algorithm, whereas only gravity statistical values were considered in classical matching algorithms. Therefore, with the proposed algorithm, the number of measurements necessary for matching can be effectively reduced, while improving the matching accuracy and success rate. In addition, a multiscale neighborhood search strategy based on contour constraints was designed to improve the matching efficiency, whereas a point-by-point global search was widely used in classical matching algorithms. Marine gravity maps of the South China Sea were used to construct the simulation tests. Simulation results show that under various conditions, the new algorithm has lower requirements for the number of measurements, measuring accuracy, and matching areas while exhibiting a higher navigation accuracy, matching success rate, and matching efficiency. Thus, the proposed algorithm could provide a new option for future practical applications of gravity matching aided navigation.

This paper proposes an integrated dispatching framework for matching drivers with riders in ride-hailing systems. The goal is to compute matching solutions that maximize social welfare and benefit both sides of the market, such that the sustainable growth of the ride-hailing system is ensured. The proposed framework integrates data-driven proactive guidance strategies with batched matching optimization to increase social welfare, improve matching rate and reduce rider wait time. Proactive guidance strategies are computed by leveraging short-term demand forecasts based on historical data. Taken the resulting guidance strategies as inputs, the batched matching algorithm computes optimal bipartite matching between drivers and riders in a batch. Using New York City taxi data from 2016 March 1st to March 31st as input, we conduct a numerical study to evaluate the performance of the proposed framework and compare it with existing approaches in the literature. Our results show that the proposed framework improves social welfare for up to 50%. It also increases the matching rate by an average of 20% and reduces the average rider wait time by over 15%. This implies a strong potential for the proposed dispatching framework to improve service quality in ride-hailing systems.

Free-space detection is considered as one of the most important tasks in intelligent vehicle, which remains the essential requirement for obstacle detection and path planning. As a part of Internet of Things, vehicle-based device produces a large amount of data requiring to be processed in realtime. Therefore, the present study proposes a fast free-space detection system with a mobile platform based stereo vision. The current system processes the image-based perception signal in terminal equipment, which avoids lots of data transmissions between cloud and vehicle. In terms of flexibility and robustness, a fast spare disparity matching algorithm is suggested to be real-timely processed in a low-power mobile platform. Based on the spare disparity map, the current work improves the u-disparity space algorithm as the u-log-disparity space with the aim to detect the free-space for significantly lowering the running time. Besides, we also provide a complete system design including hardware and software. The experiments demonstrate that our system is precise and robust at complex real-time driving environment.

Map-centric SLAM utilizes elasticity as a means of loop closure. This approach reduces the cost of loop closure while still provides large-scale fusion-based dense maps, when compared to the trajectory-centric SLAM approaches. In this paper, we present a novel framework for 3D LiDAR-based map-centric SLAM. Having the advantages of a map-centric approach, our method exhibits new features to overcome the shortcomings of existing systems, associated with multi-modal sensor fusion and LiDAR motion distortion. This is accomplished through the use of a local Continuous-Time (CT) trajectory representation. Also, our surface resolution preservative matching algorithm and Wishart-based surfel fusion model enables non-redundant yet dense mapping. Furthermore, we present a robust metric loop closure model to make the approach stable regardless of where the loop closure occurs. Finally, we demonstrate our approach through both simulation and real data experiments using multiple sensor payload configurations and environments to illustrate its utility and robustness., Comment: Submitted to IEEE Transactions on Robotics

A variety of novel mobile applications are developed to attract the interests of potential users in the emerging 5G-enabled vehicular networks. Although computation offloading and task scheduling have been widely investigated, it is rather challenging to decide the optimal offloading ratio and perform adaptive task scheduling in high-dynamic networks. Furthermore, the scheduling policy made by the network operator may be violated, since vehicular users are rational and selfish to maximize their own profits. By considering the incentive compatibility and individual rationality of vehicular users, we present POETS, an efficient partial computation offloading and adaptive task scheduling algorithm to maximize the overall system-wide profit. Specially, a two-sided matching algorithm is first proposed to derive the optimal transmission scheduling discipline. After that, the offloading ratio of vehicular users can be obtained through convex optimization, without any information of other users. Furthermore, a non-cooperative game is constructed to derive the payoff of vehicular users that can reach the equilibrium between users and the network operator. Theoretical analyses and performance evaluations based on real-world traces of taxies demonstrate the effectiveness of our proposed solution.

Bike-sharing systems usually suffer from out-of-service events due to bike underflow or overflow. We propose to recruit workers to rebalance station loads. We partition the complex rebalancing problem in temporal and spatial domains. The temporal domain is divided into a sequence of slices with a fixed duration. In each slice, we allocate a pair of overflow/underflow stations to a worker such that the cost is minimized, which is NP-hard. A 3-approximation algorithm is proposed. We further investigate the worker shortage case and extend the matching algorithm to consider the number of unsatisfied users. Then, the configuration dynamic in the sequence of slices is captured by determining the rebalancing target for each rebalancing operation. We investigate heuristic approaches to minimize the total number of bike movements. Furthermore, we extend our scheme to dockless BSSs using clustering techniques. We simulate our algorithms on both real-world and synthetic datasets. Experiment results show that our approaches can reduce the average total detour per slice. In worker shortage, considering the number of unsatisfied users could improve the long-term performance of rebalancing. Besides, we find that our scheme could maintain worker satisfaction over multiple time slices, which indicates the sustainability of our rebalancing scheme.

We consider a Cognitive Radio based Internet of Things (CR-IoT) system consisting of multiple Primary IoT Devices (PDs) and Secondary IoT Devices (SDs). We focus on the impact of the IoT devices’ heterogeneous traffic pattern on the energy efficiency and on the age of information (AoI) performance, and aim to maximize the sum of energy efficiency of the SDs under their individual constraints of the average age of information (AoI), via optimally deciding the transmit power and channel allocation. We first derive the closed-form expressions of the energy efficiency and the average AoI, and subsequently explore their convexity and monotonicity to the transmit power. Following these characterizations, an optimal transmit power optimization algorithm (TPOA) is proposed for the SDs, and the channel allocation problem is reformulated as a matching game based on the utilities calculated from TPOA. To improve the system performance, we introduce the virtual price charged in each channel. Taking advantage of the correlation between two adjacent matchings, we propose a semi-distributed, low-complexity adaptive matching algorithm to construct an -stable matching, and the effectiveness of the proposed matching algorithm is verified through Monte Carlo simulations.

Unmanned aerial vehicle (UAV) base stations (BSs) can help meet the dynamic traffic demand of flash mobile crowds, but user movements also pose a significant challenge on fasttracking for avoiding service interruption. This paper presents a novel paralLEl Optimal deeP echo stAte netwoRk preDiction (LEOPARD) approach that can fast and accurately learn the movement of a user equipment (UE) to reduce its impact on the link performance from the UE to the UAV-BS. Improving the current learning technique of deep echo state network (ESN), LEOPARD consists further three key optimization and learning techniques. First, we develop a Bayesian-Optimization Algorithm (BOA)-based hyper-parameters adjustment method for improving movement prediction accuracy. Secondly, the Message Passing Interface (MPI) technique is integrated into the design of LEOPARD to reduce the time complexity caused by BOA. Last, we design a Kuhn-Munkres (KM)-based matching algorithm to save the re-positioning energy consumption of multiple UAV-BSs. As shown in our simulation results, the prediction accuracy of the proposed LEOPARD, combining DeepESN, BOA, and MPI techniques, is 78% and 67% better than the state-of-the-art shallow ESN and the original deep ESN, respectively.

In an ultra-dense network (UDN, how to associate the mobile user equipments (UEs with the appropriate low power nodes (LPNs is a very essential and challenging problem, especially when the UEs are moving and may suffer from frequent handovers. In this paper, the UE association problem is investigated from a long-term perspective, where the UE's mobility pattern, i.e., the moving velocity and direction, is taken into consideration. To predict the UE's positions based on their previous activity data, a practical scheme, i.e., the classification and batch online learning method (CBOM, is proposed for achieving the UE's velocity and direction transition matrices. Then the UE association problem with the objective of maximizing the UEs' expected achievable rate while minimizing the number of handovers is formulated. Since the proposed problem is a NP-hard mixed integer nonlinear programming problem, we reformulate it into a matching game, and propose a mobility-based long-term matching algorithm to find the near-optimal solutions. We prove that our proposed algorithm is convergent and has low computational complexity. Simulation results show our two proposed schemes outperform the existing schemes in terms of the achieved data rate, the number of handovers, and the overall system utility.

We investigate whether joining the European Monetary Union and losing the ability to set monetary policy affected the economic growth of 12 Eurozone countries. We use the synthetic control approach to create a counterfactual scenario for how each Eurozone country would have evolved without adopting the Euro. We let this matching algorithm determine which combination of other developed economies best resembles the pre-Euro path of twelve Eurozone economies. Our estimates suggest that there were some mild losers (France, Germany, Italy, and Portugal) and a clear winner (Ireland). Nevertheless, a GDP decomposition analysis suggests that the drivers of the economic gains and losses are heterogeneous.

Service prioritization brings reasonable allocation of network resources and improves the overall quality of experience (QoE) of users, but it has not been thoroughly investigated in information centric networking (ICN). Existing works lack adaptability and they cannot ensure specific content provider (CP) get well caching service which is one of the most important functions in ICN. In this paper, we firstly propose a service prioritization scheme to flexibly provide different caching services for heterogenous CPs to improve the overall network efficiency. The main idea is to allocate dedicated cache space for paying CPs and provide prioritized caching service for them, while normal CPs only enjoy the normal caching service. The scheme can be divided into two phases. First, we select a group of nodes with higher importance as core nodes based on network topology, and pair each edge node to a core node following the two-sided many-to-one matching algorithm. Second, we dynamically allocate and manage the dedicated cache space for core nodes. We model the allocation of dedicated cache space and convert it into a convex optimization problem to solve. After that, a practical caching strategy and system design are implemented in the ndnSIM simulator. Finally, we evaluate our scheme and conduct comparative experiments with the most representative work diff-caching, simulation results show that our scheme outperform it in terms of both delay and cache hit ratio.

The internet of satellites (IoS), containing multiple low earth orbit (LEO) satellite constellations, can support tremendous traffic, massive connectivity, and vast coverage. But it also puts forward higher demands for resource management due to the high dynamics of the IoS networks, especially in the malicious jamming environment, where the jammers launch jamming attacks to reduce the efficiency and reliability. Thus, this paper investigates the problem of resource management in malicious jamming environment for IoS, which is divided into three sub-problems: traffic prediction problem, anti-jamming decision problem and resource matching problem. To solve these problems, we proposed a distributed resource management framework (DRMF), which consists of three sub-algorithms. Firstly, the traffic prediction algorithm (TPA) is proposed to deeply mine and accurately predict the traffic rule. Meanwhile, the dynamic anti-jamming algorithm (DAA) is developed to make anti-jamming decision autonomously. Then, based on the outputs obtained by TPA and DAA, the distributed resource matching algorithm (DRMA) is proposed for IoS, and the satellites with insufficient resource can apply for assistance from neighboring satellites with excess resource, thereby improving the safety and efficiency of the entire IoS network. Finally, experiment results and algorithm analysis verify the proposed scheme has better performance than the existing algorithms.

This paper is aimed at studying the online education and wireless network collaboration on electronic music creation and performance under artificial intelligence (AI). This paper uses a fuzzy clustering algorithm (FCA), designs the sensor network-related equipment, and uses AI to design an electronic music creation system. The analysis of simulation experiments suggests that under the premise of increasing the number of neighbors, the Mean Absolute Error (MAE) and Mean Squared Error (MSE) of collaborative filtering and fuzzy C -means clustering algorithms show a downward trend. However, with the same number of neighbors, the filtering matching algorithm is greater than FCA regarding the mean values of MAE and MSE. Meanwhile, on the electronic music performance system of AI, the digital module is designed and the sound data are imaged on the oscilloscope, and the collaboration of electronic music online education and wireless network is completed. The following conclusion is drawn: modularizing the creative mode of intelligent electronic music has achieved higher computational efficiency. Through the oscilloscope, the sound feature is converted into the image structure, and the corresponding sound and image mode is formed, which realizes the purpose of online electronic music intelligent matching and optimizes the effect of online education. In the AI environment, the matching degree of verification electronic music curriculum resources is better than traditional matching algorithms, and the accuracy is higher.

The underwater glider (UG) is a new type of long-endurance marine vehicle that is currently widely used in marine environment surveillance. However, the limited capability of underwater communication and the uncertainty of the marine environment leads to a large deviation between the dead-reckoned trajectory and the real trajectory of UG. Therefore, a localization and navigation method based on ocean current data is proposed in this study. Firstly, the working principle and configuration of UG are illustrated, and the dynamic model is also analyzed. Then, the depth-averaged current is derived with motion parameters of UG, and the ocean current data from the Hybrid Coordinate Ocean Model is also introduced and processed. Meanwhile, to make an accurate localization, the DAC is stratified according to the layer weight extracted from the processed ocean current model to obtain layered DAC (LDAC). Subsequently, because the sample intervals of sensors are asynchronous with LDAC, a new matching algorithm based on sample time difference is presented to match the information of sensors and LDAC. Furthermore, the matched data is applied to localization for accurate dead reckoning. Finally, based on the data collected from two experiments, the LDAC-aided localization method proposed in this paper is compared with other benchmark positioning methods, including dynamic model-aided localization, sea current-aided localization, and acoustic beacon-aided localization methods. The results indicate that the proposed new method achieves better localization performance than other benchmark methods. In addition, the proposed method could be applied to other autonomous vehicles with communication constraints.

Distinguishing target object under occlusions has become the forefront of research to cope with grasping study in general. In this paper, a novel framework which is able to be utilized for a parallel robotic gripper is proposed. There are two key steps for the proposed method in the process of grasping occluded object: generating template information and grasp detection using the matching algorithm. A neural network, trained by the RGB-D data from the Cornell Grasp Dataset, predicts multiple grasp rectangles on template images. A proposed matching algorithm is utilized to eliminate the influence caused by occluded parts on scene images and generates multiple grasp rectangles for objects under occlusions using the grasp information of matched template images. In order to improve the quality of matching result, the proposed matching algorithm improves the SIFT algorithm and combines it with the improved RANSAC algorithm. In this way, this paper obtains suitable grasp rectangles on scene images and offers a new thought about grasping detection under occlusions. The validation results show the effectiveness and efficiency of this approach.

Due to the increasing cost of data storage and computation, more and more graphs (e.g., web graphs, social networks) are outsourced and analyzed in the cloud. However, there is growing concern on the privacy of these outsourced graphs at the hands of untrusted cloud providers. Unfortunately, simple label anonymization cannot protect nodes from being re-identified by adversary who knows the graph structure. To address this issue, existing works adopt the k-automorphism model, which constructs $$(k-1)$$ symmetric vertices for each vertex. It has two disadvantages. First, it significantly enlarges the graphs, which makes graph mining tasks such as subgraph matching extremely inefficient and sometimes infeasible even in the cloud. Second, it cannot protect the privacy of attributes in each node. In this paper, we propose a new privacy model (k, t)-privacy that combines the k-automorphism model for graph structure with the t-closeness privacy model for node label generalization. Besides a stronger privacy guarantee, the paper also optimizes the matching efficiency by (1) an approximate label generalization algorithm TOGGLE with $$(1+\epsilon )$$ approximation ratio and (2) a new subgraph matching algorithm PGP on succinct k-automorphic graphs without decomposing the query graph.

Temporal graphs are graphs whose edges are associated with timestamps. Subgraph matching on temporal graphs retrieves temporal subgraphs whose edge timestamps satisfy user-specified temporal orders. In this paper, a temporal query pattern is composed of a query graph with an arbitrary structure and a partial order on the edge set of the query graph. Moreover, the time span between the minimum and the maximum edge timestamps in a matching is required to be less than or equal to a specified threshold. This paper proposes a temporal subgraph matching algorithm based on two key techniques. First, a memory-efficient index structure called TO-tree is designed to compactly store all necessary information required for finding all temporal subgraph matchings. The TO-tree constructed for a temporal query pattern is much smaller than the temporal graph because unnecessary information is mostly excluded from the TO-tree by three powerful filters. The second technique is a temporal subgraph matching enumeration method that runs on the TO-tree instead of on the temporal graph. This enumeration method expands temporal subgraph matchings in an edge-by-edge manner. Since the TO-tree can fit into the main memory, the enumeration method runs very fast on the TO-tree. An extensive experimental evaluation has been carried out. The experimental results show that the TO-tree index structure is memory-efficient, which in turn enables a fast temporal subgraph matching enumeration. Overall, our algorithm is at least 3X faster than the baseline algorithm adapted from the state-of-the-art non-temporal subgraph matching algorithm CECI and is at least 4X faster than the temporal subgraph matching algorithm HASSE .

This article presents a simple yet effective algorithm for automatically transferring face colors in portrait videos. We extract the facial features and vectorize the faces in the input video using Poisson vector graphics, which encodes the low-frequency colors as the boundary colors of diffusion curves, and the high-frequency colors as Poisson regions. Then, we transfer the face color of a reference image/video to the first frame of the input video by applying optimal mass transport between the boundary colors of diffusion curves. Next the boundary color of the first frame is transferred to the subsequent frames by matching the curves. Finally, with the original or modified Poisson regions, we render the video using an efficient random-access Poisson solver. Thanks to our efficient diffusion curve matching algorithm, transferring colors for the vectorized video takes less than 1 millisecond per frame. Our method is particularly desired for frequent transfer from multiple references due to its information reuse nature. The simple diffusion curve matching also greatly improves the performance of video vectorization, since we only need to solve an optimization problem for the first frame. Since our method does not require correspondence between the reference image/video and the input video, it is flexible and robust to handle faces with significantly different geometries and postures, which often pose challenges to the existing methods. Moreover, by manipulating Poisson regions, we can enhance or reduce the highlight and contrast so that the reference color can fit into the input video naturally. We demonstrate the efficacy of our method on image-to-video transfer and color swap in videos.

Recently, robust video transmission system that can eliminate the cliff effect in digital video transmission has attracted great interest from both academia and industry. By linearizing the whole system, robust video transmission is intrinsically scalable to channel conditions in mobile networks. However, the heterogeneity of user devices in terms of viewing resolution has not been well studied for robust video broadcast systems. In this paper, we propose a spatial scalability enabled robust video broadcast (SSRVB) system, aiming at accommodating diverse users with both heterogeneous resolutions and heterogeneous channel conditions. In SSRVB, a novel spatial decomposition method based on linear projection is first designed for robust video transmission. Then the transmission distortion minimization problem with joint subcarrier matching and power allocation is formulated. A near-optimal low-complexity subcarrier matching algorithm based on auction theory and an optimal power allocation strategy are also proposed. Furthermore, an iterative algorithm is designed to solve the problem of joint resource allocation. Simulation results demonstrate that SSRVB can achieve an average of 3 dB gain when compared with the reference schemes (i.e., ECast, MCast, discrete wavelet transform (DWT) based scheme, and scalable video coding (SVC) scheme) in terms of average peak signal-to-noise ratio under heterogeneous scenarios.

The performance of feature matching algorithms is well known to be one of the main Achilles heels of visual SLAM algorithms, and particularly for point-based visual SLAM. Which is prone to fail in low-textured ,enarios like man-made environments where points are insufficient. Yet, many environments in which, despite being low textured, can still reliably estimate line-based geometric primitives. The line-based structural features in the Manhattan world encode useful geometric information of parallelism, orthogonality, and coplanarity in the scene. By fully exploiting these structural features, we propose a novel monocular SLAM system merging feature points and structure lines, which can provide a more accurate estimation of camera poses. To integrate the structure lines into the framework of the system, we have made efforts in the detection, parameterization, feature fusion, and optimization modules of the structure lines. First, we used the consistency of the direction of the structure lines and the vanishing points to extract the structure lines. In the optimization module, we incorporated the error model of the structure lines into the nonlinear optimization framework, and proposed a new optimization strategy. Finally, a complete SLAM system based on points and structure lines is designed. With structure lines as a new observation, the robustness of the matching algorithm between consecutive frames in low-texture scenes is increased, ensuring continuous updating of the tracking thread when the feature points are lost. Secondly, the dominant directions of structure lines can provide global effective constraints to reduce the accumulated orientation errors and the position drift in consequence. Experiments in man-made environments have demonstrated that the proposed system outperforms existing state-of-the-art monocular SLAM systems in terms of accuracy and robustness.

Image matching is a method of matching by analyzing the gray scale and texture information of the reference image and the image to be matched. Firstly, the scale invariant feature transform (SIFT) algorithm has long descriptor time and poor real time, a nonlinear dimension reduction method (LLE) based on local linear embedding is proposed to preserve the nonlinear information in the original data space as much as possible, shorten the running time of the algorithm, and improve the matching accuracy. Second, aiming at the problem that the Euclidean distance takes a large amount of calculation in the matching process, Manhattan distance is proposed to calculate the similarity between the reference image and the image to be matched, so as to further reduce the algorithm time. Through the improved LLE-SIFT algorithm, experimental results show that the algorithm has a high matching rate and improves the matching speed.

With rapid advances in internet and computing technologies, sharing economy paves a new way for people to “share” assets and services with others that disrupts traditional business models across the world. Specifically, rapid growth of additive manufacturing (AM) enables individuals and small manufacturers to own machines and share under-utilized resources with others. Such a decentralized market calls upon the development of new analytical methods and tools to help customers and manufacturers find each other and further shorten the AM supply chain. This paper presents a bipartite matching framework to model the resource allocation among customers and manufacturers and leverage the stable matching algorithm to optimize matches between customers and AM providers. We perform a comparison study with Mix Integer Linear Programming (MILP) optimization as well as the first-come-first-serve (FCFS) allocation strategy for different scenarios of demand-supply configurations (i.e., from 50% to 500%) and system complexities (i.e., uniform parts and manufacturers, heterogeneous parts and uniform manufacturers, heterogeneous parts and manufacturers). Experimental results show that the proposed framework has strong potentials to optimize resource allocation in the AM sharing economy.

As the popularity of online social networks has grown, more and more users now hold multiple virtual accounts at the same time. Under these circumstances, identifying multiple social accounts belonging to the same user across different social networks is of great importance for many applications, such as user recommendation, personalized services, and information fusion. In this paper, we mainly aggregate user profile information and user behavior information, then measures and analyzes the attributes contained in these two types of information to implement across social networks user identification. Moreover, as different user attributes have different effects on user identification, this paper therefore proposes a two-level information entropy-based weight assignment method (TIW) to weigh each attribute. Finally, we combine the scoring formula with the bidirectional stable marriage matching algorithm to achieve optimal user account matching and thereby obtain the final matching pairs. Experimental results demonstrate that the proposed two-level information entropy method yields excellent performance in terms of precision rate, recall rate, F -measure ( F 1 ), and area under curve (AUC).

This paper combines the research of wireless sensor networks and sports training and proposes a wireless sensor network-based intelligent sports training system. According to the requirements of the system, this design uses the wireless sensor network system as the platform for development and the ZigBee module for wireless communication. The advantage of this system is to transmit the obtained information to the ZigBee coordinator module, and after the processing of information and the resultant decision, a nonwearable unmonitored motion training model based on visual sensing is proposed. The motion terminal collects video data streams of user motion actions and extracts features to establish HMM motion recognition algorithm to achieve recognition of motion actions, automatic counting, and intelligent scoring functions. The template matching algorithm based on dynamic time regularization and weighted Euclidean distance realizes a universal real-time motion recognition algorithm with high standard and low latency and can guide the user’s motion action based on similarity calculation. The intelligent sports training system is designed and developed to maintain a high-quality human-computer interaction experience with a real-time feedback client and uploads sports data to a cloud server via the HTTP protocol, which supports real-time sports proximity query and training plan development on the website. After practical application tests, the intelligent sports training system based on the wireless sensor network proposed in this paper is stable and reliable and adds fun and competitiveness to boring sports. The research of this paper has some reference value for the application of wireless sensor networks and the research of the motion recognition algorithm.

In this paper, a two-dimensional direction-of-arrival (DOA) estimation algorithm based on nested acoustic vector-sensor (AVS) array is introduced. The minimum unit interval of the used nested AVS array is integral multiple of half-wavelength of signal. Firstly, four selection matrices are used to recombine four received vectors, by which four special cross-covariance matrices are obtained to form an extended block covariance matrix. Then, a set of parameters on elevation angles are estimated by traditional estimation of signal parameter via rotational invariance technique (ESPRIT) algorithm. Using these parameters, unambiguous elevation angles can be obtained without spectral peak search and added eigenvalue decomposition. At last, using the estimated elevation angles, the azimuth angles can be estimated by array manifold matching algorithm. The proposed algorithm can distinguish more signals than some existing ESPRIT algorithms. Many simulation results can prove the performance of the proposed algorithm in improving the accuracy of DOA estimation. Simulation results also show that the unit interval of optimal nested AVS array should be larger than half-wavelength of received signal.

Mobile Crowd Sensing is an emerging sensing paradigm that employs massive number of workers’ mobile devices to realize data collection. Unlike most task allocation mechanisms that aim at optimizing the global system performance, stable matching considers workers are selfish and rational individuals, which has become a hotspot in MCS. However, existing stable matching mechanisms lack deep consideration regarding the effects of workers’ competition phenomena and complex behaviors. To address the above issues, this paper investigates the competition-congestion-aware stable matching problem as a multi-objective optimization task allocation problem considering the competition of workers for tasks. First, a worker decision game based on congestion game theory is designed to assist workers in making decisions, which avoids fierce competition and improves worker satisfaction. On this basis, a stable matching algorithm based on extended deferred acceptance algorithm is designed to make workers and tasks mapping stable, and to construct a shortest task execution route for each worker. Simulation results show that the designed model and algorithm are effective in terms of worker satisfaction and platform benefit. IEEE

In many metropolitans, especially during rush hours on holidays, thousands of riders will initiate travel orders at the same time, and the existing carpool matching model cannot handle largescale travel orders quickly enough. For handling this problem, a fast and efficient multi-objective carpool matching algorithm (MOCMA) is put forward, which generates a set of different matching schemes suitable for different practical scenarios. First, the idea of partition is adopted to gather riders and drivers with similar journeys, and the relationship matrix construction algorithm (RMCA) is proposed; then from the perspective of riders and drivers, the maximum service quality and the maximum shared mileage are two objectives, and a set of non-dominated solution sets are generated using MOCMA; finally, the simulation experiment results show that MOCMA proposed is suitable for different practical scenarios, the matching success rate is as high as 99.7%, and it has significant advantages over MOEA/D, SPEA2, and FastPGA.

Pore-based fingerprint recognition has been researched for decades. Many algorithms have been proposed to improve the recognition accuracy of the system. However, the accuracies are always improved at the cost of speed. This article proposes a novel method to compare the pores in high-resolution fingerprint images using the popular coarse-to-fine strategy. A multiple spatial pairwise local co-occurrence descriptor is proposed to improve the calculation of the similarities between pores. It calculates multiple local co-occurrence statistics for each pore using its neighbors. The proposed method can establish correspondences between pores more accurately. The refinement of the correspondences is then achieved by using a local topology-preserving matching algorithm. The algorithm uses rotational invariant local structures and pore pair local topology similarities to calculate the cost of each correspondence. It can remove the mismatches more accurately and efficiently. The experimental results on two high-resolution fingerprint image databases show that the proposed algorithm perform well in both accuracy and speed comparing to the existing algorithms.

Distributed energy resources, together with information and communication technologies, have transformed the traditional electricity consumers into proactive consumers, namely prosumers. Prosumers can exchange their surplus energy with consumers through peer-to-peer (P2P) energy sharing. In this paper, the framework of P2P energy exchange in active distribution networks is developed using a multi-round double auction (MRDA) with average pricing mechanism (APM) integrated with distributional locational marginal price. The advantages of the proposed P2P framework include, 1) modeling and integration of the costs of voltage regulation, congestion, and power loss into the payments of agents for each transaction; 2) the entire distribution network clustered into multiple zones with transactions cleared at different levels, which decreases the additional costs for successful transactions, reduces the computational time, and increases the number of successful transactions; and 3) the matching algorithm encourages more prosumers and consumers to participate in P2P energy sharing and increases the efficiency and benefit from P2P market. The proposed MRDA-APM framework is validated by testing on the 33-node and 141-node distribution test systems. Simulation results demonstrate the effectiveness of the proposed mechanism for P2P energy exchange from both technical and computational viewpoints.

The emergence of ridesplitting as a form of ridesourcing reduces the use of vehicles on the road. When connecting multiple ridesplitting orders and single orders to the sharing path, it can achieve higher sharing efficiency. This paper aims to further improve the vehicle sharing rate, and explore the impact of multi-mode sharing using the order data of Haikou, China provided by DiDi Chuxing. A shareability network combining the ridesplitting network and connection network is built based on the order data. We propose the on-demand matching algorithm with four matching objectives to obtain the dispatching strategy. The results show that the percentage of shared trips can reach 99%, the vehicle saving rate can reach 83%, and the average number of shared trips served by shared vehicles can reach about 6 with the time interval 20 min and maximum delay 300 s. When the maximum delay is 300 s, the percentage of orders that can be shared by multiple modes is about 30%. The average delay, idling time and waiting time of shared orders are slightly higher than the corresponding maximum delay, and increase with the increase of the maximum delay, while the change of saving time is the opposite. The proposed algorithm considers the impact of the maximum delay, which, compared with the maximum matching algorithm, has a significant improvement.

The particle filter algorithm for geomagnetic matching has been combined with Pedestrian Dead-Reckoning (PDR) for indoor positioning, but the current particle filter algorithm still has the problems of blindness and diversity. In this paper, we propose a matching algorithm based on particle filter and gait detection, which can effectively reduce the blindness of particles and increase the diversity of particles. Firstly, we improve the gait detection algorithm based on accelerometer. Secondly, we use the improved complementary filtering algorithm to obtain the user's heading information. Thirdly, the results of gait and heading plus some errors are used as the state model of particle filter. Finally, particles are regenerated through the state model when every time a gait is detected. In this way, the generation position of particles is controlled by the error of gait detection so that the blindness of particles is reduced, and the diversity of particles is guaranteed by regenerating particles every iteration. We use two kinds of magnetic map based on magnetic field vector and magnetic field intensity, and develop an Android application to test the algorithm. The experimental results show that the average positioning accuracy of the proposed method is 0.52 m using magnetic map composed of magnetic field vector, which is better than other methods.

On-demand peer-to-peer ridesharing services provide flexible mobility options and are expected to alleviate congestion by sharing empty car seats. An efficient matching algorithm is essential to the success of a ridesharing system. The matching problem is related to the well-known dial-a-ride problem, which also tries to find the optimal pickup and delivery sequence for a given set of passengers. In this paper, we propose an efficient dynamic tree algorithm to solve the on-demand peer-to-peer ridesharing matching problem. The dynamic tree algorithm benefits from given ridesharing driver schedules and provides satisfactory runtime performances. In addition, an efficient pre-processing procedure to select candidate passenger requests is proposed, which further improves the algorithm performance. Numerical experiments conducted in a small network show that the dynamic tree algorithm reaches the same objective function values of the exact algorithm, but with shorter runtimes. Furthermore, the proposed method is applied to a larger size problem. Results show that the spatial distribution of ridesharing participants influences the algorithm performance. Sensitivity analysis confirms that the most critical ridesharing matching constraints are the excess travel times. The network analysis suggests that small vehicle capacities do not guarantee overall vehicle-kilometer travel savings.

Binocular stereo vision is a commonly applied computer vision technique with a wide range of applications in 3D scene perception. However, binocular stereo matching algorithms are computationally intensive and complicated. In addition, some traditional platforms are unable to meet the real-time and energy efficient dual requirements. In this paper, we proposed a hardware/software co-design FPGA (Field Programmable Gate Array) approach to overcome these limitations. Based on the characteristics of binocular stereo vision, we modularize the system functions to achieve the hardware/software partitioning. This accelerates the data processing on the FPGA, while simultaneously performing data control on the ARM (Advanced RISC Machine) cores. The parallelism of the FPGA allows for a full-pipeline design that is synchronized with an identical system clock for the simultaneous running of multiple stereo processing components, thus improving the processing speed. Furthermore, to minimize hardware costs, the collected images and data are compressed prior to matching, while the precision is subsequently enhanced during post-processing. The proposed system was evaluated on the PYNQ-Z2 development board, with experimental results revealing its high real-time performance and low power consumption for a 100M clock frequency. Compared with existing designs, the simple yet flexible system demonstrated a higher image processing speed and less hardware resource overhead (thus lower power consumption). The average error rate of the BM matching algorithm was also improved, particularly with the limited PYNQ-Z2 hardware resource. The proposed system has been opened on GitHub.

In this letter, we present an intelligent reflecting surface (IRS) selection strategy for multiple IRSs aided multiuser multiple-input single-output (MISO) systems. In particular, we pose the IRS selection problem as a stable matching problem. A two stage user-IRS assignment algorithm is proposed, where the main objective is to carry out a stable user-IRS matching, such that the sum rate of the system is improved. The first stage of the proposed algorithm employs a well-known Gale Shapley matching designed for the stable marriage problem. However, due to interference in multiuser systems, the matching obtained after the first stage may not be stable. To overcome this issue, one-sided (i.e., only IRSs) blocking pairs (BPs) are identified in the second stage of the proposed algorithm, where the BP is a pair of IRSs which are better off after exchanging their partners. Thus, the second stage validates the stable matching in the proposed algorithm. Numerical results show that the proposed assignment achieves better sum rate performance compared to distance-based and random matching algorithms., Comment: 5 pages and 4 figures, submitted for publication

Digital video is critical visual evidence in various fields and is easily manipulated under different techniques such as the popular video copy-move forgery. In the past decades, although machine intelligence has been widely adopted to detect the forgery in digital images automatically, It still remains a very challenging detection task for carefully-crafted copy-move forgery in digital video for three reasons: (i) A video of medium length containing hundreds of frames already incurs a prohibitive computational cost; (ii) Similar backgrounds in contiguous frames are easily mistakenly detected as copy-move forgery regions, resulting to a large number of false alarms; (iii) Most state-of-the-art methods cannot detect video copy-move inter-frame or intra-frame forgeries; To effectively address these issues, a fast forgery frame detection method for video copy-move inter/intra-frame identification is proposed: (i) The sparse feature extraction and matching speed-up the algorithm processing and reduce the time cost greatly (Defect (i)); (ii) The adaptive two-pass filtering and copy-move frame-pair matching can address the similarity problem (Defect (ii)) to locate truly forgery frame-pairs (FFP); (iii) Based on the results of these FFP, the type of video copy-move forgery detection can be identified (Defect (iii)). Furthermore, the copy-move frame-pair matching algorithm locates truly FFP, thus further reducing the computation cost and false alarm for detecting the inter/intra-frame forgery efficiently and effectively (Defect (i)). Finally, based on the truly FFP, the video can be checked for forgery or original. If there is no truly FFP, the video is considered as the original one. Otherwise, the video is checked if the forgery is inter-frame (i.e., truly FFP frames are two different frames) or intra-frame (the same frame). The experimental results show that our proposed algorithm achieves higher detection accuracy and higher robustness (false alarm = 2 and F1 = 0.90) in the whole GRIP dataset than the existing state-of-the-art methods under various adverse conditions.

The fast and precise positioning of lithium battery is crucial for effective manufacturing of mass production. In order to acquire position information of lithium batteries rapidly and accurately, a novel dual-template matching algorithm is proposed to properly locate and segment each battery for fast and precise mass production. Initially, an image down-sampling method is applied to build up a multi-layer image pyramid for speeding up target search, and a novel mixed matching template is designed to increase the matching precision. A row of lithium batteries is likely tilt during rolling, and the images of batteries captured by the CCD camera are distorted, which may generate a negative effect on next procedure. Hence, a two-level correction algorithm for battery angle and location is applied to obtain rough areas of the batteries and improve the accuracy of template matching. Lastly, the comparison with other state-of-the-art algorithms is done to locate each battery in a row with high speed and precision. The precision rates of the proposed algorithm, improved SAD algorithm, and YOLOv3 algorithm are 99.44%, 95.98%, and 93.64 for normal battery images and 97.86%, 89.19%, and 85.10 for tilted battery images, respectively. Compared with improved SAD matching algorithm and YOLOv3 algorithm, the positioning accuracy of the proposed method is significantly increased, and the matching robustness is improved in spite of large battery inclination angle.

The Particle Tracking Velocimetry (PTV) has a wide range of applications in the field of fluid and particle flow measurement. Due to the uncertainty caused by the optimal probability in the matching algorithm, the long-term tracking of multiple particles will eventually lead to lower accuracy with increasing time. In this study, a full-field high-accuracy PTV algorithm for pebble flow measurement is developed. Based on a two-dimensional pebble flow experiment, improved matching algorithms for pebble flow is proposed. Analyzing two kinds of errors in the experimental data—unidentified centroids and misidentified centroids, two amending methods are developed. The original PTV algorithm for pebble flow reached an average accuracy of 90% under 5 different flowrates. After adopting the three-point co-circular amending method, the accuracy is increased to 98%. Finally, this value exceeds 99% when the LP polygon amending method is applied. The adaptability and stability of the algorithm have also been tested.

Cloud Manufacturing (CM) as a successful manufacturing business model and a major driver of Industry 4.0 has attracted a lot of attention in recent years. CM idea aims to streamline the on-demand provisioning of manufacturing resources and capabilities as services, providing end-users with flexible and scalable services accessible through global networks. This idea created many opportunities and challenges. One of the critical challenges is resource allocation, which determines who interacts with whom and how in the CM platform. The type of the platform is a determining factor for the selection of the appropriate resource allocation. To analyze the impact of the allocation on the utilities, the paper models the behavior of the manufacturing providers and consumers based on their preference attributes. Then, the paper discusses the influence of the platform, matching algorithm, and resource availability on the utility of the manufacturing providers and consumers. As a result, the paper presents a framework to obtain managerial insights to decide about the appropriate matching algorithm under different situations. The framework suggests Consumer as Proposer Deferred Acceptance algorithm for public platforms when the resources are greater than or equal to the demand, and Provider as Proposer Deferred Acceptance algorithm when the resources are less than the demand in the same platform. In private platform, Consumer-oriented Kuhn-Munkres is suggested when the resources are greater than or equal to the demand, and Provider-oriented Kuhn-Munkres when the resources are less than the demand.