1,084 research outputs found

    Wireless MIMO Switching: Weighted Sum Mean Square Error and Sum Rate Optimization

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    This paper addresses joint transceiver and relay design for a wireless multiple-input-multiple-output (MIMO) switching scheme that enables data exchange among multiple users. Here, a multi-antenna relay linearly precodes the received (uplink) signals from multiple users before forwarding the signal in the downlink, where the purpose of precoding is to let each user receive its desired signal with interference from other users suppressed. The problem of optimizing the precoder based on various design criteria is typically non-convex and difficult to solve. The main contribution of this paper is a unified approach to solve the weighted sum mean square error (MSE) minimization and weighted sum rate maximization problems in MIMO switching. Specifically, an iterative algorithm is proposed for jointly optimizing the relay's precoder and the users' receive filters to minimize the weighted sum MSE. It is also shown that the weighted sum rate maximization problem can be reformulated as an iterated weighted sum MSE minimization problem and can therefore be solved similarly to the case of weighted sum MSE minimization. With properly chosen initial values, the proposed iterative algorithms are asymptotically optimal in both high and low signal-to-noise ratio (SNR) regimes for MIMO switching, either with or without self-interference cancellation (a.k.a., physical-layer network coding). Numerical results show that the optimized MIMO switching scheme based on the proposed algorithms significantly outperforms existing approaches in the literature.Comment: This manuscript is under 2nd review of IEEE Transactions on Information Theor

    A New Two-Dimensional Functional Material with Desirable Bandgap and Ultrahigh Carrier Mobility

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    Two-dimensional (2D) semiconductors with direct and modest bandgap and ultrahigh carrier mobility are highly desired functional materials for nanoelectronic applications. Herein, we predict that monolayer CaP3 is a new 2D functional material that possesses not only a direct bandgap of 1.15 eV (based on HSE06 computation), and also a very high electron mobility up to 19930 cm2 V-1 s-1, comparable to that of monolayer phosphorene. More remarkably, contrary to the bilayer phosphorene which possesses dramatically reduced carrier mobility compared to its monolayer counterpart, CaP3 bilayer possesses even higher electron mobility (22380 cm2 V-1 s-1) than its monolayer counterpart. The bandgap of 2D CaP3 can be tuned over a wide range from 1.15 to 0.37 eV (HSE06 values) through controlling the number of stacked CaP3 layers. Besides novel electronic properties, 2D CaP3 also exhibits optical absorption over the entire visible-light range. The combined novel electronic, charge mobility, and optical properties render 2D CaP3 an exciting functional material for future nanoelectronic and optoelectronic applications

    ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond

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    Oversmoothing is a common phenomenon in a wide range of Graph Neural Networks (GNNs) and Transformers, where performance worsens as the number of layers increases. Instead of characterizing oversmoothing from the view of complete collapse in which representations converge to a single point, we dive into a more general perspective of dimensional collapse in which representations lie in a narrow cone. Accordingly, inspired by the effectiveness of contrastive learning in preventing dimensional collapse, we propose a novel normalization layer called ContraNorm. Intuitively, ContraNorm implicitly shatters representations in the embedding space, leading to a more uniform distribution and a slighter dimensional collapse. On the theoretical analysis, we prove that ContraNorm can alleviate both complete collapse and dimensional collapse under certain conditions. Our proposed normalization layer can be easily integrated into GNNs and Transformers with negligible parameter overhead. Experiments on various real-world datasets demonstrate the effectiveness of our proposed ContraNorm. Our implementation is available at https://github.com/PKU-ML/ContraNorm.Comment: ICLR 202

    THE APPLIED OF KINITECH ISOKINETIC REHABILITATION AND TESTING UNIT IN THE STRENGTH TRAI ING OF ELITE ATHLETES AFTER KNEE JOINT INJURY

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    Knee joint injury is one of common injuries in sports, it affects the improvement of sports performance, reduce the number of years for sports, even ends athlete's sports career. This study, which aims to apply the isokinetic training in the most excellent Chinese female athletes of softball after knee joint injury, verifies that isokinetic training not only improves muscle strength of athletes but also is a very effective way in the rehabilitation after knee joint injury

    Research on bearing fault diagnosis technology based on machine learning

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    As industrial equipment complexity continues to rise, the importance of bearings within these systems has become more critical, given their pivotal role in equipment functionality. Bearing faults can result in severe production accidents and safety issues. Hence, there is an urgent need for advanced bearing fault diagnosis technology. This study concentrates on rolling bearings, analyzing their structural characteristics and key parameters to classify fault types—inner race faults, rolling element faults, and outer race faults. Utilizing a dataset of 80 sets of bearing factory data, time and frequency domain analyses are conducted, establishing seven feature parameters (five in the time domain and two in the frequency domain). This data is organized into a 7-dimensional matrix for subsequent analysis and model development. The K-Means algorithm is chosen for its effectiveness in automatically recognizing fault patterns in rolling bearings. Training on the 7-dimensional matrix identifies four clustering centers corresponding to normal conditions, inner race faults, rolling element faults, and outer race faults. The fault diagnosis system is implemented using Python, and algorithm optimization improves efficiency. The study concludes with insights drawn from the analysis and proposes optimization methods, which contributing to advancing bearing fault diagnosis technology, particularly addressing industrial equipment reliability and safety concerns

    Study on separation characteristics of two-phase flow in double helical separator

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    The helical separator plays an important role in improving the working efficiency of electric submersible pump. Separation efficiency of two-phase flow in double helical separator is studied by numerical simulation and theoretical calculation. It is found that the separation efficiency of helical separator increases with the increase of gas-liquid ratio and flow rate. At the same time, under the condition of constant helical number and gas-liquid ratio, the separation efficiency is best when the even difference of pitch is 10 mm

    Impact of signaling schemes on iterative linear minimum-mean-square-error detection

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    In this paper, we study the iterative detection problem for a coded system with multi-ary modulation. We show that, with iterative linear minimum-mean-square-error (LMMSE) detection, superposition coded modulation (SCM) can provide performance superior to that with other traditional signaling schemes used in trellis coded modulation (TCM) and bit-interleaved coded modulation (BICM). This finding provides a useful guideline for system design considering inter-symbol interference (ISI) and other forms of interference. Simulation results are provided to illustrate the efficiency of the iterative LMMSE detection with different signaling schemes. © 2008 IEEE
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