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Wi-Fi fingerprint-based indoor localization has recently attracted significant research interest since Wi-Fi devices are widely deployed and practical, and no additional infrastructure is required. However, the Received Signal Strength (RSS) is significantly different on heterogeneous devices, and this difference has a negative impact on localization results. In this paper, we propose a multi-fingerprint and multi-classifier fusion (MFMCF) localization method to improve the localization accuracy and solve the problem of heterogeneous hardware. First, the individual feature set of original RSS fingerprint, signal strength difference (SSD) fingerprint and hyperbolic location fingerprint (HLF) are fused as a composite fingerprint set (CFS), and then the data dimension is reduced by linear discriminant analysis (LDA). Second, three representative machine learning algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF) are selected and trained to construct an integrated fusion model. Finally, in order to get more accurate predictions, in the online phase, a selective strategy based on entropy is proposed by calculating the entropy of each classifier’s prediction result. Experiments show that MFMCF is an effective scheme to solve the localization problem of heterogeneous devices and improve the localization accuracy. [ABSTRACT FROM AUTHOR]

Device-to-Device (D2D) communication with short communication distance is an efficient way to improve spectrum efficiency and mitigate interference. To realize the optimal resource configuration including wireless channel matching and power allocation, a distributed resource matching scheme is proposed based on deep reinforcement learning(DRL). The reward is defined as the difference of achieve rate of D2D users and the consumed power, which is limited by the Signal to Interference plus Noise Ratio (SINR) of the other cellular users on the current channel. The proposed algorithm maximizes the D2D throughput and energy efficiency in a distributed manner, without online coordination and message exchange between users. The considered resource allocation problem is formulated as a random non-cooperative game with multiple players (D2D pairs), where each player is a learning agent, whose task is to learn its best strategy based on locally observed information, multi-user communication resource matching algorithm is proposed based on a Double Deep Q-network (DDQN), where the total cellular throughput and user energy efficiency could converge to the Nash equilibrium (NE) under the mixed strategy. Simulation results show that the proposed algorithm can improve the communication rate and energy efficiency of each user by selecting the optimal strategy, and has better convergence performance compared with existing schemes. [ABSTRACT FROM AUTHOR]

Highlights: [•] We design an industrial wireless data acquisition system based on IEEE 802.11n protocol. [•] Simplify the initial synchronization protocol by the console aided method. [•] Present an adaptive frequency and amplitude compression algorithm to improve the limited bandwidth utilization. [ABSTRACT FROM AUTHOR]

Summary Indoor location based on Wi‐Fi fingerprint has attracted extensive attention recently, since it only requires a mobile device and existing network; it does not require additional infrastructure and hardware. However, localization using only received signal strength fingerprint is susceptible to dynamic environments and device heterogeneity. Localization using signal strength differences fingerprint and hyperbolic location fingerprint which extracted from received signal strength fingerprint is a powerful measure to overcome the device heterogeneity. Existing fusion‐based methods do not fully use of the mutual fusion function of multiple fingerprints, which leads to low positioning accuracy. In order to further improve the positioning accuracy, we propose a multifingerprint multiclassifier and two‐layer fusion weight positioning model. The proposed approach first creates a multiple fingerprints group by gleaning signal strength differences fingerprint and hyperbolic location fingerprint from received signal strength fingerprint. In order to deal with the accuracy decline caused by a single classifier in complex scenes, three typical classifiers, random forest (RF), K‐nearest neighbor (KNN), and support vector machine (SVM), are adopted, and a fusion weight selection algorithm is proposed to improve the localization accuracy by choosing fusion weights intelligently from the two‐layer fusion weights. Experiment results show that the proposed scheme is effective to resolve device heterogeneity, and the position accuracy is improved about 16.9% and 5.8%, respectively, compared with single fingerprint and classifier schemes. The results show that, compared with the single fingerprint scheme, the position accuracy is improved at least 16.9% and 28.4% for the homogeneous and heterogeneous equipments, respectively; as for the proposed multiclassifier fusion method, it achieves at least 5.8% and 8.7% accuracy improvement for the cases with homogeneous and heterogeneous equipments, respectively, compared with other traditional classifiers such as RF, KNN, and SVM. [ABSTRACT FROM AUTHOR]

In recent years, the development of new scintillation materials with red and near-infrared light emission has had significant advantages based on the development of an avalanche photodiode with high quantum yield. This study used a hydrothermal method to synthesize rare-earth-doped Cs2NaScCl6 crystals, in which a red emission of Cs2NaScCl6:Sm was investigated with high X-ray sensitivity and low detection limit. In addition, a tunable wavelength range of radioluminescence can be achieved, in which a near-white light emission can be generated by precisely controlling the proportion of Sm3+ and Tb3+ doping concentrations. Meanwhile, a Cs2NaScCl6:7%Sm scintillation film has been successfully prepared by direct coating with a thickness of 150 μm, which achieves a spatial resolution of 11.9 lp mm−1 based on X-ray imaging experiments. Therefore, this perovskite material has shown potential application as an eco-friendly multifunctional imaging material. [ABSTRACT FROM AUTHOR]

In this paper, a mobile anchor node assisted RSSI localization scheme in underwater wireless sensor networks (UWSNs) is proposed, which aims to improve location accuracy and shorten location time. First, to improve location accuracy, we design a support vector regression (SVR) based interpolation method to estimate the projection of sensor nodes on the linear trajectory of the mobile anchor node. The proposed method increases the accuracy of the nonlinear regression model of noisy measured data and synchronously decreases the estimation error caused by the discreteness of measured data. Second, to shorten location time, we develop a curve matching method to obtain the perpendicular distance from sensor nodes to the linear trajectory of the mobile anchor node. The location of the sensor node can be calculated based on the projection and the perpendicular distance. Compared with existing schemes that require the anchor node to travel at least two trajectories, the proposed scheme only needs one-time trajectory to locate sensor nodes, and the location time is shortened with the reduction in the number of trajectories. Finally, simulation results prove that the proposed scheme can obtain more accurate sensor node location in less time compared with the existing schemes. [ABSTRACT FROM AUTHOR]

Background: The content of stone cells in pears has a great influence on taste. Stone cells are formed by the accumulation of lignin. The treatment of exogenous calcium can affect the lignin synthesis, but this Ca-mediated mechanism is still unclear. In this study, the author performed a comparative transcriptomic analysis of callus of pears (Pyrus x bretschneideri) treated with calcium nitrate Ca (NO3)2 to investigate the role of calcium in lignin synthesis. Results: There were 2889 differentially expressed genes (DEGs) detected between the Control and Ca (NO3)2 treatment in total. Among these 2889 DEGs, not only a large number of genes related to Ca single were found, but also many genes were enriched in secondary metabolic pathway, especially in lignin synthesis. Most of them were up-regulated during the development of callus after Ca (NO3)2 treatment. In order to further explore how calcium nitrate treatment affects lignin synthesis, the author screened genes associated with transduction of calcium signal in DEGs, and finally found CAM, CML, CDPK, CBL and CIPK. Then the author identified the PbCML3 in pears and conducted relevant experiments finding the overexpression of PbCML3 would increase the content of pear stone cells, providing potential insights into how Ca treatment enhances the stone cell in pears. Conclusions: Our deep analysis reveals the effects of exogenous calcium on calcium signal and lignin biosynthesis pathway. The function of PbCML3 on stone cells formation was verified in pear. [ABSTRACT FROM AUTHOR]

In this paper, we consider a two-tier three phases simultaneous wireless information and power transfer (SWIPT)-enabled down-link cooperative non-orthogonal multiple access (NOMA) system. In inner tier, the farther central user decodes the information of the outer users, and then harvests energy from the base station to serve the outer users as a dedicated relay. In the cooperative phase, the nearer outer user is the potential relay to the further one; the users need to decode the information of the other outer user and also harvest energy for information transmission. The expressions of the achievable rate and outage probability of users are derived based on the transmit power allocation and the power splitting for the SWIPT system. A two-stage optimization problem is formulated by a resource allocation strategy, where the problem is constrained by the power allocations, the requirements of successive interference cancelation, and the outage probability. Since the primary problem of the outer optimization problem is non-convex, the problem is solved by the successive convex approximation (SCA). The rate maximization algorithm based on the dual-loop optimization is further developed. The simulation results validate that the proposed resource allocation scheme is capable of satisfying the performance requirements in a cooperative NOMA system, and the results also demonstrate the advantage of the cooperative NOMA system when implementing the SWIPT on the benchmarks of orthogonal multiple access (OMA). [ABSTRACT FROM AUTHOR]

Detecting wood broken defects through machine vision is challenging due to the similar appearance of defect and defect-free regions on images. Laser profilometer is a reasonable solution, nevertheless, imperfect point cloud representation, such as slope profile, incontinuity of tiny defects and similarity between broken defects and sound area, poses obstacles. To overcome these challenges, this study proposes a multi-line detection method based on bidirectional long- and short-term memory network (Bi-LSTM) for real-time wood broken defect detection. The feature that represents the extent of surface damage in line-level is designed by residual extraction and sorting operation. The Bi-LSTM combines adjacent information to exaggerate semantic information of detection line. Context information extracted by Bi-LSTM are concatenated for multi-line detection to reduce computation complexity. Finally, detection results are modified by considering the information of adjacent lines of point cloud. Experimental results show that the proposed method achieves real-time detection with high accuracy. • Bi-LSTM based multi-line detection method is proposed to detect wood broken defect. • The feature is designed to represent the extent of surface damage in line-level. • Bi-LSTM is refined to increase the utilization rate of context information. • Bi-LSTM exaggerates the semantic information of detection line. • The proposed method achieves real-time detection with high accuracy. [ABSTRACT FROM AUTHOR]

Steel plate is one of the most valuable steel products which is highly customized in specification according to the demands of users. In this case, the outbound scheduling of steel plates is a challenging issue since its efficiency and complexity are impacted by both steel plate shuffling and truck loading sequencing. To overcome this challenge, we propose to jointly optimize steel plate shuffling and truck loading sequencing (SPS-TLS) by utilizing the data of steel plates and trucks collected by Industrial Internet of Things (IIoT). The SPS-TLS problem is firstly transformed as an orders scheduling problem which is formulated as a mixed-integer linear programming (MILP) model. Then an alternating iteration algorithm based on deep reinforcement learning (AltDRL) is proposed to solve the SPS-TLS problem. In AltDRL, the deep Q network (DQN) with prioritized experience replay (PER) and the heuristic algorithm are combined to iteratively obtain the near-optimal shuffling position of blocking plates and truck sequence. Experiments are executed based on data collected from a real steel logistics park. The results confirm that AltDRL can significantly reduce the number of plate shuffles and improve the outbound scheduling efficiency of steel plates. • Steel plate shuffling and truck loading sequencing are jointly optimized. • Proposing a mathematical model for the joint optimization problem. • An algorithm based on deep reinforcement learning is proposed. • Proposed algorithm evaluated using real steel logistics park data. [ABSTRACT FROM AUTHOR]

The meter reading with machine vision greatly improves the efficiency of industrial monitoring. However, the pointer and scales of the meter can be occluded by rain or dirt, which greatly reduces the accuracy of the meter reading recognition. To solve this problem, we propose a generative adversarial network (PMS-GAN) with pointer generation and main scale detection for occluded meter reading. Specifically, dilated convolution block is designed to correlate separated pointer features. Then multi-scale feature fusion mechanism is proposed to guarantee the precision of pointer generation and main scale detection with guidance of semantic information. Moreover, feature enhancement mechanism is proposed to construct the long-range relationship for generating pointer under high occlusion. Finally, the reading is accomplished by calculating local angle with generated pointer and detected main scales. Experiments show that PMS-GAN can generate more intact pointer and detect main scales to guarantee the success and accuracy of occluded meter reading. • PMS-GAN is proposed to read meter with occluded pointer and scales. • DC block is designed to correlate the separated features for pointer generation. • The MFFM and FEM are proposed to enhance the generation ability of PMS-GAN. • The proposed reading calculation guarantees the accuracy of reading occluded meter. • The proposed PMS-GAN improves success rate and accuracy for reading occluded meter. [ABSTRACT FROM AUTHOR]

The peg-in-hole task with object feature uncertain is a typical case of robotic operation in the real-world unstructured environment. It is nontrivial to realize object perception and operational decisions autonomously, under the usual visual occlusion and real-time constraints of such tasks. In this paper, a Bayesian networks-based strategy is presented in order to seamlessly combine multiple heterogeneous senses data like humans. In the proposed strategy, an interactive exploration method implemented by hybrid Monte Carlo sampling algorithms and particle filtering is designed to identify the features' estimated starting value, and the memory adjustment method and the inertial thinking method are introduced to correct the target position and shape features of the object respectively. Based on the Dempster–Shafer evidence theory (D-S theory), a fusion decision strategy is designed using probabilistic models of forces and positions, which guided the robot motion after each acquisition of the estimated features of the object. It also enables the robot to judge whether the desired operation target is achieved or the feature estimate needs to be updated. Meanwhile, the pliability model is introduced into repeatedly perform exploration, planning and execution steps to reduce interaction forces, the number of exploration. The effectiveness of the strategy is validated in simulations and in a physical robot task. [ABSTRACT FROM AUTHOR]

A resource allocation approach is proposed for wireless powered cooperative cognitive radio network (WP-CCRN) in this paper, where multiple secondary users (SUs) capture both the wireless energy and the authorized spectrum from primary user (PU) to communicate with a secondary access point (SAP). In return, SUs serve as relay nodes to help the primary transmitter (PT) forwarding data to the primary receiver (PR). For the sake of the stable cooperation between PU and SUs, we present a game-based fair resource allocation algorithm (GFRAA) that takes into account the utilities of both sides. The Stackelberg game is applied for solving the resource allocation problem between PU and SUs, where PU acts as a leader while SUs are followers. To solve the non-convex optimization problem in the primary network, a new variable namely allocated energy is introduced to transform the primal problem into a convex problem. Meanwhile, the relationship among SUs is modeled as a Supermodular game. Since SUs are rational and selfish, a fairness evaluation mechanism is designed to adjust the fairness among SUs. Simulation results unveil the trade-off between the fairness of SUs, the throughput of PU, and the sum-throughput of SUs. The superiority of the GFRAA has been validated by comparing with the other two algorithms. [ABSTRACT FROM AUTHOR]

Optimal power management of shipboard power system for failure mode (OPMSF) is a significant and challenging problem considering the safety of system and person. Many existing works focused on the transient-time recovery without consideration of the operating cost and the voyage plan. In this paper, the OPMSF problem is formulated considering the mid-time scheduling and the faults at bus and generator. Two-side adjustment methods including the load shedding and the reconfiguration are coordinated for reducing the fault effects. To address the formulated non-convex problem, the travel equality constraint and fractional energy efficiency operation indicator limitation are transformed into the convex forms. Then, considering the infeasibility scenario affected by faults, a further relaxation is adopted to formulate a new problem with feasibility guaranteed. Furthermore, a sufficient condition is derived to ensure that the new problem has the same optimal solution as the original one. Because of the mixed-integer nonlinear feature, an optimal management algorithm based on Benders decomposition (BD) is developed to solve the new one. Due to the slow convergence caused by the time-coupled constraints, a low-complexity near-optimal algorithm based on BD is proposed. The results verify the effectivity of the proposed methods and algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a framework is proposed to improve the effectiveness of the long-term-evolution vehicle-to-vehicle (LTE-V2V) communication system, where the uplink channel of the cellular user equipment (CUE) is reused by multiple V2V links. Since the orthogonal channels occupied by each CUEs are multiplexed by multiple V2V links, the serious interference is generated in the co-tier layer and cross-tier layer. In order to minimize the co-channel interference between CUE link and V2V links, an optimized resource sharing scheme is proposed. Firstly, Fuzzy C-means (FCM) algorithm is proposed to perform the clustering based on the geographical location characteristics of V2V links. In the FCM algorithm, the adjacent V2V links are grouped into a cluster to facilitate the solution of the channel allocation problem. Assuring the V2V links in the same cluster do not share the same channel, the adjacent V2V links are avoided to reuse the same channel. On this basis, the use of reasonable channel allocation scheme can effectively reduce the co-channel interference. Considering that the channel state information (CSI) of mobile links is slowly faded, the total system capacity is maximized by leveraging the CSI while the reliability can be guaranteed for all V2V links. The outage probability constraint is used to ensure the reliability of all V2V links. The channel assignment problem of V2V links is formulated as a weighted two-dimensional matching problem, and the interference bipartite graph is constructed. Finally, Kuhn Munkras (KM) algorithm is introduced to allocate the uplink orthogonal channel of CUEs for the V2V links in the same class in order to maximize the total system capacity. Numerical simulation results show that the algorithm can improve the total system capacity. [ABSTRACT FROM AUTHOR]