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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]

Surface defect detection of sawn timber is a critical task to ensure the quality of wooden products. Current methods have challenges in considering detection accuracy and speed simultaneously, due to the complexity of defects and the massive length of sawn timbers. Specifically, there are scale variation, large intraclass difference and high interclass similarity in the defects, which reduce the detection accuracy. To overcome these challenges, we propose an efficient multilevel-feature integration network (EMINet) based on YOLOv5s. To obtain discriminative features of defects, the cross fusion module (CFM) is proposed to fully integrate the multilevel features of backbone. In the CFM, the local information aggregation is designed to enrich the detailed information of high-level features, and the global information aggregation is designed to enhance the semantic information of low-level features. Experimental results demonstrate that the proposed EMINet achieves better accuracy with fast speed compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

With the advent of Industry 4.0 and the popularization of smart terminal equipment, the interaction between industrial field information systems and production equipment has intensified. To meet the real-time transmission of time-triggered flow and the coordinated transmission of best effort flow, time-sensitive network-related technologies are used to implement flow queue forwarding by strictly following the gate control list. First, response-time analysis method is proposed to predict the upper bound of delay under a scheduling model following IEEE 802.1Qbv. Second, according to response-time analysis, a deadline monotonic scheduling algorithm with temporary priority expansion is proposed to divide the priority into more levels, which is not limited by the queue type, so as to ameliorate the transmission sequence of switch export flow. Finally, an adaptive priority adjustment scheduling algorithm with temporary priority expansion is designed to construct the optimal scheduling method further improving the scheduling success rate and reducing worst-case end-to-end delays. Compared with similar algorithms, the algorithm proposed improves the scheduling success rate by at least 30%, reduces the total delay by at least 21% and the TT flow delay by 11% in high network utilization conditions. [ABSTRACT FROM AUTHOR]

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]

In this paper, we introduce edge computing technologies (ECT) to improve the quality of smart grid demand-side communications and simultaneously optimize bandwidth along with computing power resources, further reducing the costs to the utility company (UC). First, we employ the edge node (EN) with data computing capability as relay, meanwhile, constructing a analytical model of computing resources and bandwidth. Second, the electricity costs is built based on the packet loss model and the regulation errors in direct load control. Then the UC decides the computing resources and the bandwidth for relaying to minimize its total costs, and the EN sets the base price to maximize its profit. Furthermore, we consider the interactions between the UCs and the ENs as a Stackelberg game. Simulation results demonstrate a significant reduction in costs to the utility company and an exaltation in profits of the relays. [ABSTRACT FROM AUTHOR]

The number of people in a specific region can provide valuable information in many aspects including Heating Ventilating and Air Conditioning (HVAC) systems adjustment, firefighting rescue, consumer service updating and personnel management, etc. Most of the traditional studies for people counting utilize all the collected data for analyzing, which results in the heavy burden of data processing and extra sensing devices. In this paper, we propose a CSI-based sensor-free crowd counting scheme utilizing only existing WiFi infrastructure in a non-intrusive, low-cost and precise manner. Different from other systems, the best performed data in the received end are chosen to be analyzed for the purpose of obtaining the optimal results. The framework is proposed based on an intuition that different numbers of people wandering in the environment cause distinct impacts on WiFi signals. To begin with, the best performed data are processed and denoised with a wavelet-based denoising algorithm. Then, four features that can depict the relationship between the number of people and data fluctuation are extracted and analyzed. Eventually, the selected features are labeled and taken as inputs of several machine learning algorithms. And the complicated people counting problem is transformed into a classifying issue through extracting and training the features. The experimental results validate the effectiveness of the proposed easy and low-cost crowd counting framework. [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]

Body falling is a major risk of the older people. This paper proposes a non-contact scheme for detecting human body state using infrared array sensors, which has the advantages of low cost, easy implementation and high detection accuracy. For achieving real-time monitoring, rather than simple motion analysis, the system identifies suspicious points of body falling by simple positioning, which attempts to achieve the real-time implementation and reduces the analysis of irrelevant data. In addition, considering the influence of the radiation temperature result of the human body, the falling characteristics are included, and the clustering algorithm is proposed to tackle the problem of inaccurate determination of the area caused by the Otsu area detection. Then, a two-layer threshold system model is proposed for excluding jogging movements that are similar to the falling feature. Finally, the features are engaged with the random forest classifier in order to improve the classification rate. Experiments have shown that the proposed fall detection scheme is highly feasible and behaves good fall detection accuracy. [ABSTRACT FROM AUTHOR]

This article investigates the power control scheme in an uplink clustering network when the vehicle density is high. In the dynamic communication environment, the vehicle-to-vehicle (V2V) channel model is proposed to evaluate the extra Doppler shifted impact on the uncertain channel gains. Since the mobile wireless channels are fast time varying and the vehicle mobility is usually high, a robust optimization problem is formulated to maximize the network-wide sum rates constrained with the predefined outage probability. To perform the optimization, the original problem with probability constraints is first converted to the deterministic problem, using the known statistical characteristics (i.e., the mean and variance of V2V channel gains). Second, the successive convex approximation is proposed to transform the nonconvex problem into the convex problem. Third, the dual decomposition is used to determine the optimal solution. Finally, an iterative algorithm is used to determine the optimal transmission power of each user. The proposed framework is evaluated using numerical simulations with different V2V configurations. The numerical simulation results show the effectiveness and the robustness of the proposed algorithm when applying on dynamic vehicular network scenes. [ABSTRACT FROM AUTHOR]

Along with methods such as Hidden Markov Model and Linear Discriminant Analysis, environmental sensors including temperature, humidity or carbon dioxide are used to estimate indoor occupancy, which have various applications. Most of the previous studies have neglected the time-dependent character of the indoor occupancy information or real-time response of the system. In this paper, we propose to use low invasive, fast-sampling infrared array sensors to collect data from the actual scene and establish the Inhomogeneous Hidden Markov Model to capture the time dependence of occupancy for buildings occupancy estimation. First, to avoid raw sensor datas susceptibility to external influences, the non-negative matrix factorization is adopted to reduce the dimension of the raw matrix and eliminate interferences caused by environmental changes. Second, the Softmax Regression Model is used to calculate the emission probability matrix for clarifying the dynamic relationship between environmental parameters and indoor occupancy, which is weak. Third, the Forward algorithm and the Viterbi algorithm are applied to achieve online and off-line estimation. Experiments are made with real recorded data. Our performance results demonstrate that method we proposed is effective for indoor occupancy estimation. [ABSTRACT FROM AUTHOR]

In this paper, a two-tier uplink femtocell network operating in orthogonal frequency-division multiple access is investigated. Femtocell base stations endowed with cognitive capacity can sense the void spectrum of macrocell. We aim at an energy-saving scheme of power allocation for femtocell users. The new idea that performance of Femtocell networks can be analyzed using control theory is proposed in this paper. A simple and novel power control algorithm is designed based on the virtual Proportional-Integral (PI) controller. When considering the maximum power constraints, different from the proof of the convergence via standard interference functions, we establish a linear system with state saturation (maximum power constraint) for femtocell networks, and the power's convergence is verified in theory with iterative linear matrix inequality algorithm. Two introduced control parameters not only guarantee the stability of power control algorithm, but also can adjust the transient response and save energy. Numerical results illustrate the convergence performance and advantages of the PI algorithm. [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]

Class III peroxidases (PRXs) are plant-specific enzymes that play key roles in the responses to biotic and abiotic stress during plant growth and development. In addition, some peroxidases also play roles in plant lignification. In this study, a total of 114 PRX (designated PbPRXs) genes were identified in the pear (Pyrus bretschneideri Rehd) genome based on systematic analysis. These PRX genes were divided into 12 groups based on their phylogenetic relationships. We performed systematic bioinformatics analysis of the PRX genes, including analysis of gene structures, conserved motifs, phylogenetic relationships, and gene expression patterns during pear fruit growth. The PbPRXs are unevenly distributed on the 17 pear chromosomes and some of them on other scaffolds. Gene duplication event analysis indicated that whole-genome duplication (WGD) and segmental duplication play key roles in PRX gene amplification. Ka/Ks analysis suggested that most duplicated PbPRXs experienced purifying selection, with limited functional divergence during the duplication events. Furthermore, the analysis indicated that those highly expressed genes might play significant roles in the lignification of cells to form stone cells in pear fruit. We examined the expression of those highly expressed genes during fruit growth using quantitative real-time PCR (qRT-PCR), verifying differential expression patterns at different stages of fruit. This study provides useful information for further functional analysis of the PRX gene family in pears. [ABSTRACT FROM AUTHOR]