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The initial orientation error of a coil vector magnetometer directly determines the measurement accuracy of the magnetic field direction. The traditional initial orientation method requires auxiliary manually operated instruments. To solve this problem, we propose an electrical orientation method based on applying bias fields. To analyze quantitatively the orientation error of the proposed method, a physical model for the coil orientation indicator and measurement errors of the magnetic direction was developed that exploits measurements of the deflected field obtained after applying two oppositely directed bias fields. The simulation results show that, when the difference indicator is 0.7 nT, the measurement error of the magnetic direction is less than $9\,''$. Moreover, when the bias field strength is greater than 30% of the geomagnetic field strength during the orientation operation, the influence of the change in strength of the bias field on the measurement error of the magnetic direction diminishes significantly. The indirect effect of the measurement error of the total-field sensor on the measurement accuracy of the magnetic direction was also analyzed. To verify this method, we designed an experimental setup and conducted a comparative experiment. The results show that as the difference indicator increases, the measurement error of the magnetic direction increases accordingly. When this indicator is adjusted to about 0.7 nT, the measurement error of the magnetic direction is less than $11.16''$ , which is in agreement with theoretical results. [ABSTRACT FROM AUTHOR]

Latent Dirichlet Allocation (LDA) is a popular tool for analyzing discrete count data such as text and images, which are required to model datasets and a large number of topics, e.g., tens of thousands of topics for industry scale applications. Although distributed CPU systems have been used to address this problem, they are slow and resource inefficient. GPU-based systems have emerged as a promising alternative because of their high computational power and memory bandwidth. However, existing GPU-based LDA systems can only learn thousands of topics, because they use dense data structures, and have linear time complexity to the number of topics. In this article, we propose SaberLDA, a GPU-based LDA system that implements a sparsity-aware algorithm to achieve sublinear time complexity to learn a large number of topics. To address the challenges introduced by sparsity, we propose a novel data layout, a warp-based sampling kernel, an efficient sparse matrix counting method, and a fine-grained load balancing strategy. SaberLDA achieves linear speedup on 4 GPUs and is 6–10 times faster than existing GPU systems in thousands of topics. It can learn 40,000 topics from a dataset of billions of tokens in two hours, which was previously only achievable using clusters of tens of CPU servers. [ABSTRACT FROM AUTHOR]

Proton precession magnetometer (PPM) is a potential device to measure the low-frequency geomagnetic field in the time domain. Nevertheless, the uncontrollable noise such as power frequency noise, random noise, etc., severely affects the detection results. In this paper, aimed to boost the performance of the PPM, a novel real-time processing algorithm for the optimization of PPM output strategy based on the low-rank constraint (LRC) is proposed, and a new efficient framework, dubbed LRC-based geomagnetic data readout optimizer (LRC-GDRO), is implemented. This method aims to extract the dominant principal components of the pure magnetic signal and identify the singular values of noise. Thereby, the relative ideal signal without external noise, which still contains the ambient field information is yielded. Moreover, we develop a prototype device and compare the test results before and after using the proposed method. Consequently, compared with traditional PPM, the performance of the PPM using the proposed method has been significantly improved with a measurement accuracy of ± 0.2 nT. Furthermore, the experimental results reveal that the LRC-optimized PPM is comparable to a very high-accuracy Overhauser magnetometer in terms of 12 h geomagnetic field observation. [ABSTRACT FROM AUTHOR]

Achieving a good tradeoff between spectral efficiency (SE) and energy efficiency (EE) is important for the emerging wireless communication systems. Motivated by this, we study multiuser downlink beamforming in massive multiple-input multiple-output systems for maximizing a new metric of resource efficiency (RE), which is defined as a weighted combination of EE and SE. The new measure has more flexibility in striking the balance between SE and EE, but brings enormous difficulties in solving the resulting problem. To this end, we first investigate an uplink–downlink duality for the RE maximization. The conventional uplink–downlink duality only applies in the SE or the capacity region, and has yet not been explicitly established for the EE or the RE. In this paper, we prove with rigorous derivation that the duality has a more general form, which can be directly used to tackle the EE or RE beamforming optimization problem. Based on the duality, we then develop an optimization algorithm to realize spectral and energy efficient multiuser beamforming with either instantaneous or statistical channel state information (CSI). Numerical results finally verify that the design based on statistical CSI is able to asymptotically achieve the performance obtained with instantaneous CSI, but with much lower complexity. [ABSTRACT FROM AUTHOR]

Recently, IEEE 802.11ax, introducing the fundamental improvement of WLANs, was approved as the next generation WLAN technology. Satisfying tremendous user demands for user experience, IEEE 802.11ax will fuel the future intelligent information infrastructure to serve big data transportation and diverse smart application scenarios. In this article, we overview the key technology features of IEEE 802.11ax such as OFDMA PHY, UL MU-MIMO, spatial reuse, OFDMA random access, power saving with TWT, and STA-2-STA operation, and explain translating these features to enhance user experience, highlighting the design principles to facilitate smart environments and identifying new technological opportunities. [ABSTRACT FROM PUBLISHER]

In this paper, a special control solution called virtual voltage source (VVS) is developed for no-load tests of induction machines. This concept is applied to a 2 × 27 MVA high-power machine test bench where the test bench is requested to precisely characterize, e.g., 3 MW machines at no-load currents. The developed VVS thoroughly solves the stability issue of induction machines at low-speed ranges. A set of optimizations is presented here that systematically improves the voltage precision of the test bench. Experimental results prove that the proposed concept achieves 0.08–0.12% voltage unbalance, less than 3% current unbalance, with less than 0.3% harmonic-voltage-factor, and verified by more than 100 induction machines. [ABSTRACT FROM AUTHOR]

Automatic cell segmentation can hardly be flawless due to the complexity of image data particularly when time-lapse experiments last for a long time without biomarkers. To address this issue, we propose an interactive cell segmentation method by classifying feature-homogeneous superpixels into specific classes, which is guided by human interventions. Specifically, we propose to actively select the most informative superpixels by minimizing the expected prediction error which is upper bounded by the transductive Rademacher complexity, and then query for human annotations. After propagating the user-specified labels to the remaining unlabeled superpixels via an affinity graph, the error-prone superpixels are selected automatically and request for human verification on them; once erroneous segmentation is detected and subsequently corrected, the information is propagated efficiently over a gradually-augmented graph to un-labeled superpixels such that the analogous errors are fixed meanwhile. The correction propagation step is efficiently conducted by introducing a verification propagation matrix rather than rebuilding the affinity graph and re-performing the label propagation from the beginning. We repeat this procedure until most superpixels are classified into a specific category with high confidence. Experimental results performed on three types of cell populations validate that our interactive cell segmentation algorithm quickly reaches high quality results with minimal human interventions and is significantly more efficient than alternative methods, since the most informative samples are selected for human annotation/verification early. [ABSTRACT FROM PUBLISHER]

The affinity propagation (AP) clustering algorithm has received much attention in the past few years. AP is appealing because it is efficient, insensitive to initialization, and it produces clusters at a lower error rate than other exemplar-based methods. However, its single-exemplar model becomes inadequate when applied to model multisubclasses in some situations such as scene analysis and character recognition. To remedy this deficiency, we have extended the single-exemplar model to a multi-exemplar one to create a new multi-exemplar affinity propagation (MEAP) algorithm. This new model automatically determines the number of exemplars in each cluster associated with a super exemplar to approximate the subclasses in the category. Solving the model is NP--hard and we tackle it with the max-sum belief propagation to produce neighborhood maximum clusters, with no need to specify beforehand the number of clusters, multi-exemplars, and superexemplars. Also, utilizing the sparsity in the data, we are able to reduce substantially the computational time and storage. Experimental studies have shown MEAP's significant improvements over other algorithms on unsupervised image categorization and the clustering of handwritten digits. [ABSTRACT FROM AUTHOR]

Based on the equivalent circuit model, expressions for the power absorbed by an externally coupled input cavity of the relativistic klystron amplifier in both cold and hot cavities are derived. The matching conditions are clearly concluded from the expressions for the two cases. Under the guide of the matching conditions, the expressions are applied in the determination of the beam-loaded resonant frequency and the beam-loaded quality factor of the cavity loaded by the intense relativistic electron beam in a particle-in-cell simulation-aided approach presented in this paper. [ABSTRACT FROM PUBLISHER]

The dynamics of expanding cathode and anode plasmas has long been recognized as a limiting factor of high-power vacuum diodes. An investigation of the characteristics of plasmas in a planar explosive emission diode powered by a 275-kV, \sim110-ns pulse is presented. In addition to current and voltage measurements, an ultra fast framing camera was employed to study transient characteristics of the plasma instabilities. It was shown by optical diagnostics that the anode plasma was clearly visible for the velvet cathode. Moreover, the anode plasma expanded rapidly across the anode-cathode gap, and the light emitted from it was much more intensive than that from the cathode plasma. In particular, we find that plasma transit times from anode to cathode are hundreds of nanoseconds, a very distinct and experimentally observable phenomena described in this article. [ABSTRACT FROM PUBLISHER]