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The crystallization, microstructure and magnetic properties of Fe83NixB16−xCu1 (x = 0, 1, 2, 4) alloys are investigated by Mossbauer spectroscopy, X-ray diffractometry and magnetometry. The substitution of Ni for B in the Fe83B16Cu1 alloy enriches Fe clusters in melt-spun ribbons and promotes the nucleation of α-Fe(Ni), which is beneficial to the formation of high volume fraction of nanocrystals after crystallization. Furthermore, the addition of 1–2 at.% Ni widens the annealing temperature range for the alloys with low HC. Excellent magnetic properties with high saturation magnetic flux density (BS) of 1.83 T and low coercivity (HC) of 9.2 A/m are obtained by annealing the Fe83Ni2B14Cu1 melt-spun ribbons. In addition, the nanocrystalline alloy with x = 2 possesses a lower saturation magnetostriction (19 ppm) than that of the Ni-free alloy (25 ppm).

The magnetic properties of a series of Fe-P-C amorphous alloys were investigated. Fe80P13C7 shows the largest saturation magnetic flux density among all the Fe80P20−xCx (x = 7–9) alloys, which is supported by the hyperfine field distribution. Synchrotron X-ray diffraction was performed to explore the underlying structure mechanism. It is found that Fe80P13C7 amorphous alloy has a larger atomic volume accompanied by a larger first peak position value of the reduced pair distribution function compared with those of Fe80P12C8 and Fe80P11C9. The origin of the unusual magnetic behavior of Fe80P13C7 is thought to be the large Fe-Fe bond length.

Atomic structure evolution induced by thermal relaxation is investigated in Fe80P11C9 metallic glass by a suite of techniques. The topological structure of Fe80P11C9 metallic glasses characterized by the Voronoi polyhedrons varies with relaxation proceeding. Meanwhile, the results of partial pair distribution functions and local composition distribution indicate that the degree of the chemical order for different species changes and the solvent-solute correlation significantly enhances. Moreover, the Mossbauer spectra measurements suggest that significant element redistribution occurs during the structural relaxation. Hence, only topological structure change is insufficient to describe the structural relaxation. These results manifest that the structural relaxation can be described via heterogeneity degeneration, which is also supported by the small angle X-ray scattering measurements.

Crack is a common type of metal indication defects, which brings great hidden dangers to safety of hoisting machinery in use. Automatic metal crack detection methods could be practical in less expensive and high efficiency. In this paper, an encoder-decoder convolutional neural network is proposed, called GFU-Net, which can automatically predict pixel-level crack segmentation by end-to-end method. GFU-Net introduces the guide transformer module on U-Net’s base to strengthen the fusion between corresponding features and applies the Deeply-Supervised Net (DSN), which places features of each convolutional stage under the integrated straight supervision. The experiments show that our work outperforms all other models we test in this article and detects metal cracks from low-contrast images effectively and explicitly.

With the development of communication and information technologies, intelligent mobile terminals such as smartphones and laptops have become an indispensable part of people's work and life. However, due to the limited resources of smart mobile devices, such as limited battery power and processing speed, the running efficiency of mobile applications are affected. Vehicular cloud as a cloud service provider can be utilized to provide resources in order to improve energy and execution efficiency of intelligent mobile terminals. When a vehicular cloud receives requests from multiple mobile users, how to achieve an efficient task scheduling is a great challenge, that is, how to obtain a higher service success ratio and reduce the task completion time as much as possible. In this paper, we propose a priority based multi-user task scheduling (PMTS) strategy for vehicular cloud services. Transmission time and execution time of tasks are analyzed comprehensively to calculate the priority of different tasks, and tasks are scheduled in order of priority. A rescheduling scheme is designed with considering the deadline of each task to improve the service success ratio. The performance of PMTS is evaluated by extensive simulations, and experimental results demonstrate that our strategy achieves a higher service ratio and acceptable task completion time compared with the existing scheme.

Fe83.5B16.5-xCux (x = 0, 0.7, 1.2, 1.5) amorphous alloys were rapidly annealed under various cyclic numbers (C). The crystallization behavior and its effects on the microstructure and soft magnetic properties of nanocrystalline alloys have been investigated. It is found that the cyclic annealing is more effective to refine and uniform α-Fe grains by introducing multiple rapid heating than the non-cyclic annealing. The saturation magnetic flux density of the Fe83.5B15Cu1.5 nanocrystalline alloy increases from 1.79 T to 1.82 T and the coercivity decreases from 13.1 A/m to 7.5 A/m simultaneously when the cyclic numbers change from C = 1 to 6. Moreover, the rapid cyclic annealing with C = 6 enlarges the annealing temperature ranges for the Fe83.5B16.5 and Fe83.5B15Cu1.5 nanocrystalline alloys with low HC by ∼20 K compared to that with C = 1. The Mossbauer spectroscopy confirms that the rapid cyclic annealing can inhibit the aggregation of B atoms in the amorphous matrix, which is beneficial to enhance the magnetic interaction between α-Fe nanocrystals. However, when the cyclic numbers further rise to C = 10, the mean grain size of the Fe83.5B15Cu1.5 nanocrystalline alloy begins to increase, resulting in the deterioration of soft magnetic properties.

Atom migration, phase structure and magnetic properties in the Fe82Si2B11P4Cu1 amorphous alloy isothermally annealed at various heating rates are investigated. Annealing the samples at a high heating rate promotes the formation of Cu-rich regions and delays the phase separation process to high temperature, which is conductive to increase the volume fraction of the nanocrystalline phase and refine nanograins during the crystallization. When the alloy is isothermally annealed at 758 K for 180 s, the saturation magnetic flux density increases from 1.74 T to 1.79 T and the coercivity decreases from 7.8 A/m to 4.4 A/m with the heating rate increasing from 0.5 K/s to 20 K/s. Meanwhile, it confirms that the occupation of Si atoms in the lattice of bcc-Fe can be suppressed by the annealing with high heating rate. Increasing the heating rate is widely regarded as an effective method to improve soft magnetic properties of Fe-based nanocrystalline alloys. However, it is found that the content of the nonmagnetic atom enriched regions rises with the increase of heating rate, which can reduce the magnetic interaction in the nanocrystalline ribbons. Therefore, the exorbitant heating rate reaching 40 K/s will dramatically promote the aggregation of nonmagnetic atoms and deteriorate the soft magnetic properties.

The evolution of local structure and defects in the Fe81Si4B10P4Cu1 amorphous alloy during the structural relaxation has been investigated by Mossbauer spectroscopy, positron annihilation lifetime spectroscopy and transmission electron microscopy to explore their effects on magnetic properties of the nanocrystalline. The atomic rearrangements at the early stage of the structural relaxation cause the density increase of the amorphous matrix, but the subsequent atomic rearrangements contribute to the transformation of Fe3B-like atomic arrangements to FeB-like ones with the temperature increasing. As the structural relaxation processes, the released Fe atoms both from Fe3B- and Fe3P-like atomic arrangements result in the formation of new Fe clusters and the increase of Fe-Fe coordination number in the existing Fe clusters and the nucleation sites for α-Fe gradually increase, both of which promote the crystallization. However, the homogeneity of amorphous matrix will be finally destroyed under excessive relaxation temperature, which coarsens nanograins during the crystallization instead. Therefore, soft magnetic properties of the Fe81Si4B10P4Cu1 nanocrystalline alloy can be improved by pre-annealing the amorphous precursor at an appropriate temperature due to the atomic level structural optimization.

The exponential increase of the intelligent connected devices and the dramatic growth of the wireless data traffic have motivated the development of the green wireless networks as well as the Internet of Things (IoT). In this paper, we study the minimization problem of the total power to satisfy the required rate constraints in IoT, where the users simultaneously communicate through multiple independent channels. This problem is complicated due to the nonlinear data rate function based on the Shannon capacity formula. To this end, we first transfer the initial problem in power domain to an equivalent problem in rate domain instead of direct approximation for the high data rate. Then, we approximate it to a convex problem with the spectral radius constraints by the use of the Neumann expansion and nonlinear Perron–Frobenius theorem. By doing so, we achieve the close upper bound for this total power minimization problem. Moreover, we obtain the lower bound by making use of the convex relaxation technique, and finally get the global optimal solution by leveraging the branch-and-bound method. Simulation results verify that our proposed algorithms have a good approximation to the global optimal value for the power and rate allocations.

In the next-generation cognitive radio networks, numerous secondary users will share the spectrum resource with the primary users. As it may not be possible to support all the communication rate requirements, there are many supporting sets for the secondary users as long as the communication rates of the primary users are guaranteed. In this paper, we study the maximum feasible set problem to access as many secondary users as possible, under the constraints of power budgets and communication rates in cognitive radio networks. In this interesting issue, the existing literature generally removes a subset of the secondary users so that the remaining users achieve the thresholds with communication rates and power budgets. However, the removal algorithms cause more interference when there are plenty of unsupported secondary users. We leverage the spectral radius of the network characteristic matrix as the admission price to access the new secondary user. Then, we design a hybrid access control algorithm to reduce the interference time and approximate the maximum network capacity. Moreover, different supported sets produce the different energy efficiency, even having the same network capacity, while all users require the high communication rates. Numerical results demonstrate that our algorithms provide the decent energy efficiency under the communication rate constraints.

In order to improve the utilization efficiency of Internet of Things (IoT), more collaboration is preferable in the collection and exchange of data. When multiple applications/tasks request data from a sensor, we can make use of data sharing among the tasks as long as the data meets the time-sensitive QoS requirements of the tasks. This would in turn reduce both the total sensing time and the energy consumption, which is a significant concern in IoT systems. A practical question is how to design an efficient schedule to enable more data sharing and less sensing time. This paper addresses such a problem by developing algorithms with performance guarantees, respectively, for the offline and online scenarios. Two task models are studied, respectively, first-in first-out (FIFO) task model and arbitrary deadline (AD) task model. We first provide an optimal algorithm for FIFO tasks in the offline case, and then we study the online case, in which data requests arrive dynamically without prior information. For FIFO tasks, we develop an online two-competitive algorithm that always incurs a total sensing time no more than two times of the optimal solution. For AD tasks, we devise an online algorithm that is $O(\log L)$ competitive, where $L$ is the maximum length of the time duration of the tasks. Simulation results validate the efficiency of our online algorithms.

In this work, an attempt has been made to reveal critical factors dominating the crystallization and soft magnetic properties of Fe81Si x B10P8−xCu1 (x = 0, 2, 4, 6 and 8) alloys. Both melt spun and annealed alloys are characterized by differential scanning calorimetry, X-ray diffractometry, Mössbauer spectroscopy, transmission electron microscopy, positron annihilation lifetime spectroscopy and magnetometry. The changes in magnetic interaction between Fe atoms and chemical homogeneity can well explain the variation of magnetic properties of Fe81Si x B10P8−xCu1 amorphous alloys. The density of nucleation sites in the amorphous precursors decreases in the substitution of P by Si. Meanwhile, the precipitated nanograins gradually coarsen, but the inhibiting effect of P on grain growth diminishes causing the increase of the crystallinity. Moreover, various site occupancies of Si are observed in the nanocrystallites and the Si occupancy in bcc Fe decreases the average magnetic moment of nanograins. Without sacrificing amorphous forming ability, we can obtain FeSiBPCu nanocrystalline alloy with excellent soft magnetic properties by optimizing the content of Si and P in the amorphous precursors.

This paper studies the joint beamforming and power control in a multiuser multi-input single-output network by utilizing the only statistical channel distribution information. Such information consists of slowly varying covariance matrices in the beamforming network that can be employed to reduce instantaneous feedback overhead in transmission. Utilizing solely the statistical channel information, we study how to minimize the maximum outage probability under a weighted sum power constraint that guarantees max–min fairness to all users. This problem is, however, generally hard to solve due to the nonconvexity and nonlinear coupling between beamformer and power variables. First, assuming a fixed beamformer set, we use the nonlinear Perron–Frobenius theory to design a decentralized algorithm with provable geometrically fast convergence rate to compute the optimal power. Then, for the general case, we examine a certainty-equivalent margin counterpart with outage-mapped thresholds that incorporate the statistical channel information. We show that a network duality for this certainty-equivalent problem can be useful to decouple the coupling between the beamformer and power variables. This nonlinear Perron–Frobenius theory motivated approach yields a feasible beamformer and power allocation that are near-optimal as compared to Monte Carlo averaging simulations.

With rapid development of Internet of Things (IoT) and pervasive computing, indoor localization and tracking has attracted considerable attentions. This work aims at designing an effective and scalable indoor tracking system based on smart phones embedded with Wi-Fi interfaces and inertial sensors. Specifically, we first propose a zero site-survey overhead algorithm (ZSSO), which includes a step detection mechanism, a map constraint construction method and a customized particle filter. The step detection mechanism is used to count user steps based on raw data extracted from inertial sensors. The map constraint construction method is adopted to generate obstacle constraints of the indoor environment based on a two-step conversion method designed for indoor map. Finally, a customized particle filter is proposed to track user's positions continuously. Further, we propose an enhanced version of ZSSO (i.e., E-ZSSO) to improve tracking performance by incorporating with Wi-Fi fingerprint based localization technique. First, an automatic Wi-Fi fingerprint collection mechanism is developed for building the fingerprint database without extra site-survey overhead. Then, the Wi-Fi fingerprint based localization results are further adopted to speed up the convergence of the particle filter as well as to better calibrate the localization results. We have implemented the indoor tracking system in real-world environments and conducted comprehensive performance evaluation. The field testing results conclusively demonstrate the scalability and effectiveness of our proposed algorithms.

The design of high efficient security mechanisms in resource constrained multi-hop Wireless Sensor Networks (WSNs) has always been a hot topic. Recently, compressed sensing (CS) has drawn great attentions for it can jointly compress and encrypt data with low energy cost and low computational and storage overhead. However, existing work only focuses on the data confidentiality rather than data authentication, integrity, etc. In order to achieve authentication and integrity without any extra communication overhead, this paper proposes a secure data transmission mechanism based on perturbed CS by introducing perturbations for authentication. We conduct extensive experiments to evaluate the performance of the proposed mechanism; the experimental results demonstrate the proposed secure mechanism can achieve authentication and integrity in multi-hop WSNs.

Adapting the data rate is an important performance in mobile cloud networking, especially for the fast growth of intelligent terminals. We study a max-min fairness problem for the mobile cloud networking to guarantee the minimal transmit data rate, by leveraging the bit error rate (BER) with Q-function for modeling achievable data rates. We propose a distributed power control algorithm to obtain the optimal solution. Then, we address a total power minimization problem with the given rate requirement constraints. When there are plenty of users and excessive interferences, its feasibility issue is solved by making use of the max-min fairness of the networks. We propose a dynamic algorithm that adapts the rate requirements to minimize the total energy consumption and to simultaneously provide fairness guarantees. Numerical simulations show the efficient performance of the proposed algorithms.

We study the feasibility of the total power minimization problem subject to power budget and Signal-to-Interference-plus-Noise Ratio (SINR) constraints in cognitive radio networks. As both the primary and the secondary users are allowed to transmit simultaneously on a shared spectrum, uncontrolled access of secondary users degrades the performance of primary users and can even lead to system infeasibility. To find the largest feasible set of secondary users (i.e., the system capacity) that can be supported in the network, we formulate a vector-cardinality optimization problem. This nonconvex problem is however hard to solve, and we propose a convex relaxation heuristic based on the sum-of-infeasibilities in optimization theory. Our methodology leads to the notion of admission price for spectrum access that can characterize the tradeoff between the total energy consumption and the system capacity. Price-driven algorithms for joint power and admission control are then proposed that quantify the benefits of energy-infeasibility balance. Numerical results are presented to show that our algorithms are theoretically sound and practically implementable.

A key challenge in wireless networks is to control and minimize power. In this paper, we study a total power minimization problem subject to power and Signal-to-Interference-plus-Noise Ratio (SINR) constraints in wireless networks with multiple independent channels. The objective is to minimize the total transmission power of the system while the sum rate targets of channels are satisfied. However, this problem is non-convex and thus is hard to solve. Through the Shannon capacity formula, we establish a connection between power and rate, and get an equivalent transformation of this optimization problem. We transfer the original problem from power domain into rate domain, which plays an important role in using convex approximation next and algorithm design. Then, leveraging the Perron-Frobenius theorem, we reformulate the problem with spectral radius constraint. Finally we obtain an approximated convex problem with rate as variable of the initial optimization problem. Further, we propose a corresponding convex approximation algorithm to get the approximation value. Simulation results evaluate that our algorithm can achieve efficient power and rate allocations.

In recent years, the auction has been widely applied in wireless communications for spectrum allocation, so that spectrum owners can lease their unutilized spectrum to secondary users. Both primary users and secondary users can get benefit from the auction. Moreover, the spectrum utilization is improved. Existing auction mechanisms either do not consider the heterogeneity of spectrums or pay little attention to the auction's economic efficiency. In this paper, we propose a Truthful and Profit maximizing double Auction for Heterogeneous Spectrums (TPAHS), which simultaneously considers spectrum heterogeneity and economic efficiency. Moreover, different from the most existing spectrum auction mechanisms which are based on interference graph, we consider a more realistic SINR (Signal-to-Interference-plus-Noise Ratio) model. We prove that TPAHS is truthful, individual-rational, budget-balanced and the experiments show that TPAHS improves the auctioneer's profit significantly.