An error compensation method for the single pair-pole encoder has been discussed in this paper. This article analyzed offset, sensitivity error, quadrature error and ferromagnetic interference error of single-pole magnetic encoder to obtain the expression of each error. In order to facilitate error compensation, the common expression for describing the error has been summed up. The process for formatting the error can be assumed as the process of changing from circle to ellipse. Therefore the inverse of this process is the same as the process of error compensation. The experimental results show that the accuracy of magnetic encoder which used this method could reach ±1, thus the error compensation effect is obvious. The magnetic encoder which applied this method has the advantages of low-cost, high-precision and convenient to use.

Data gathering is an attractive operation for obtaining information in wireless sensor networks (WSNs). But one of important challenges is to minimize energy consumption of networks. In this paper, an integration of distributed compressive sensing (CS) and virtual multi-input multi-output (vMIMO) Keywords: data gathering, compressive sensing, virtual MIMO, energy optimizationCopyright © 2017 Universitas Ahmad Dahlan. All rights reserved. IntroductionWSNs are typically self-organizing netwoks consisiting of nunmerous low-cost, feature riched and energy-limited sensor nodes, which can be widely applied in environmental monitoring, intelligent home furnishing, military monitoring, security monitoring and other fields [1,2]. Such applications usually require that sensor nodes in surveillance region periodically sense data and report to sink noedes or base stations. So data gathering is an important operation to collect and transmit the sensed data to sink nodes. At present, WSNs have severe energy constraints, the problem for data gathering is that the transmssion of huge amounts of monitoring data causes large consumptions of nodes and reduces network life cycle.Many CS-based data gathering methods have been studied to improve the energy efficiency of WSNs [3][4][5][6][7][8][9][10][11]. Chong Luo, et al., [3] applied CS theory to tree-based and chainbased data gathering in WSNs to obtain efficient data compression. In [4], You proposed a CSbased dynamic source and transmission control algorithm to prolong the lifetime of networks. In [5], a CS model for data gathering was proposed that used spatial and temporal relativity of signals. This model reduced the quantity of transmitted data and achieved better reconstruction performance in sink nodes. In [6] and [7], combined with random walk routing and CS measurement matrices, compressive data gathering schemes were proposed. In [8] and [9], CS was applied in clustered networks, cluster heads received raw data from their member-nodes and sent them to sink by the way of single-hop network. Besides that, some researches about sparse projections [10] and joint optimization of transport cost and recovery [11] in WSNs have had some useful explorations. These CS based methods can decrease cost by data compression and reducing the number of in-network data packets. But these researches are all based on transmission model with single input and single output.As multi-antenna transmission in wireless networks can achieve spatial diversity. And spatial diversity is considered to be an effective solution to resist channel fading and reduce power consumption. Virtual MIMO mechanism with single-antenna nodes had been introduced in data gathering [12]. In [13] and [14], virtual MIMO was applied to improve data gathering cost in clustered wireless sensor networks. We can notice that above data gathering methods based on virtual MIMO need an effective data fusion, this mechanism will make systems more

Massive machine type communication (mMTC) serves an irreplaceable role in the development process of the Internet of Things (IoT). Because of its characteristics of massive connection and sporadic transmission, compressed sensing (CS) has been applied in joint user activity and data detection in the uplink grant-free non-orthogonal multiple access (NOMA) system. In previous work, greedy iterative-based multi-user detection (MUD) algorithms were developed in mMTC scenarios because of the computational benefit and competitive performance. However, conventional greedy iterative-based MUD algorithms still suffer from high computational complexity due to the process of large-size matrix inversion with the accession of massive devices into the system. In this paper, gradient information is used to address this problem. A low-complexity gradient descent-based gradient pursuit MUD (GDGP-MUD) algorithm is proposed, which uses the gradient information of error function in the process of iteration as a new updating direction, instead of the matrix inversion process. Then, a multi-step quasi-Newton MUD (MSQN-MUD) algorithm is proposed to improve the precision of detection while maintaining low complexity. In the algorithm, high-order information in the process of adjacent iteration is used effectively to update data values more accurately. Moreover, the convergence and complexity analysis of both algorithms are derived. The analysis shows that both proposed algorithms have lower computational consumption than most of the stateof-the-art greedy-based MUD algorithms. It is worth noting that in comparison to most existing CS-based MUD algorithms, the two proposed algorithms do not require the exact user sparsity level and, thus, reduce the dependence on prior knowledge. The numerical experiments demonstrate that the proposed algorithms have better real-time performance than existing greedy-based MUD algorithms with similar symbol error rate performance.INDEX TERMS Massive machine type communication (mMTC), non-orthogonal multiple access (NO-MA), multi-user detection, compressed sensing, gradient method

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The heat transfer and pressure drop characteristics for water flowing in four spiral coils with different shapes and different sizes were experimental studied. Reynolds number range from 4000 to 9000, volume flow rate range from 200 to 350 L/h and heating power range from 80-350 W. Based on the experimental results, the regularity of Reynolds number and heating power influencing on heat transfer and pressure drop characteristics was analyzed and discussed. The results indicate: the Nu increases with increasing Re, the greatest average heat transfer coefficient appears in the smaller circular spiral coil. The heat transfer coefficients increase with increasing heating power, the greatest average heat transfer coefficient also appears in the smaller circular spiral coil. The pressure drops increase with increasing Re, the pressure drop in big ellipse spiral coil is greatest. The resistance coefficients gradually decrease with increasing Re. The resistance coefficient of small circular spiral coil is always greatest, and the resistance coefficient of big circular spiral coil is smallest.