arXiv subjects

Shuai Wang

Publications and source records attributed to Shuai Wang.

At least 739 records · Page 41Linked to original sources

arXiv2019

Backscatter Data Collection with Unmanned Ground Vehicle: Mobility Management and Power Allocation

Collecting data from massive Internet of Things (IoT) devices is a challenging task, since communication circuits are power-demanding while energy supply at IoT devices is limited. To overcome this challenge, backscatter communication emerges as a promising solution as it eliminates radio frequency components in IoT devices. Unfortunately, the transmission range of backscatter communication is short. To facilitate backscatter communication, this work proposes to integrate unmanned ground vehicle (UGV) with backscatter data collection. With such a scheme, the UGV could improve the communication quality by approaching various IoT devices. However, moving also costs energy consumption and a fundamental question is: what is the right balance between spending energy on moving versus on communication? To answer this question, this paper studies energy minimization under a joint graph mobility and backscatter communication model. With the joint model, the mobility management and power allocation problem unfortunately involves nonlinear coupling between discrete variables brought by mobility and continuous variables brought by communication. Despite the optimization challenges, an algorithm that theoretically achieves the minimum energy consumption is derived, and it leads to automatic trade-off between spending energy on moving versus on communication in the UGV backscatter system. Simulation results show that if the noise power is small (e.g., -100 dBm), the UGV should collect the data with small movements. However, if the noise power is increased to a larger value (e.g., -60 dBm), the UGV should spend more motion energy to get closer to IoT users.

cs.IT↗

arXiv2019

Joint Communication and Motion Energy Minimization in UGV Backscatter Communication

While backscatter communication emerges as a promising solution to reduce power consumption at IoT devices, the transmission range of backscatter communication is short. To this end, this work integrates unmanned ground vehicles (UGVs) into the backscatter system. With such a scheme, the UGV could facilitate the communication by approaching various IoT devices. However, moving also costs energy consumption and a fundamental question is: what is the right balance between spending energy on moving versus on communication? To answer this question, this paper proposes a joint graph mobility and backscatter communication model. With the proposed model, the total energy minimization at UGV is formulated as a mixed integer nonlinear programming (MINLP) problem. Furthermore, an efficient algorithm that achieves a local optimal solution is derived, and it leads to automatic trade-off between spending energy on moving versus on communication. Numerical results are provided to validate the performance of the proposed algorithm.

cs.IT↗

arXiv2019

A positivity-preserving finite volume element method for anisotropic diffusion problems on quadrilateral meshes

In this paper, we propose a nonlinear positivity-preserving finite volume element(FVE) scheme for anisotropic diffusion problems on quadrilateral meshes. Based on an overlapping dual partition, the one-sided flux is approximated by the iso-parametric bilinear element. A positivity-preserving nonlinear scheme with vertex-centered unknowns is obtained by a new two-point flux technique, which avoids the convex decomposition of co-normals and the introduction of intermediate unknowns. The existence of a solution is proved for this nonlinear system by applying the Brouwer's theorem. Numerical results show that the proposed positivity-preserving scheme is effective on distorted quadrilateral meshes and has approximate second-order accuracy for both isotropic and anisotropic diffusion problems. Moreover, the presented scheme is applied on an equilibrium radiation diffusion problem with discontinuous coefficients. Numerical results show that the new scheme is much more efficient than the standard FVE method.

math.NA↗

arXiv2019

Massive MIMO Multicast Beamforming Via Accelerated Random Coordinate Descent

One key feature of massive multiple-input multiple-output systems is the large number of antennas and users. As a result, reducing the computational complexity of beamforming design becomes imperative. To this end, the goal of this paper is to achieve a lower complexity order than that of existing beamforming methods, via the parallel accelerated random coordinate descent (ARCD). However, it is known that ARCD is only applicable when the problem is convex, smooth, and separable. In contrast, the beamforming design problem is nonconvex, nonsmooth, and nonseparable. Despite these challenges, this paper shows that it is possible to incorporate ARCD for multicast beamforming by leveraging majorization minimization and strong duality. Numerical results show that the proposed method reduces the execution time by one order of magnitude compared to state-of-the-art methods.

cs.IT↗

arXiv2018

Arbitrarily routed mode-division multiplexed photonic circuits for dense integration

Mode-division multiplexing (MDM) is becoming an enabling technique for large-capacity data communications via encoding the information on orthogonal guiding modes. However, the on-chip routing of a multimode waveguide occupies too large chip area due to the constraints on inter-mode cross talk and mode leakage. Very recently, many efforts have been made to shrink the footprint of individual element like bending and crossing, but the devices still occupy >10x10 um2 footprint for three-mode multiplexed signals and the high-speed signal transmission has not been demonstrated yet. In this work, we demonstrate the first MDM circuits based on digitized meta-structures which have extremely compact footprints. The radius for a three-mode bending is only 3.9 μm and the footprint of a crossing is only 8x8um2. The 3x100 Gbit/s mode-multiplexed signals are arbitrarily routed through the circuits consists of many sharp bends and compact crossing with a bit error rate under forward error correction limit. This work is a significant step towards the large-scale and dense integration of MDM photonic integrated circuits.

physics.app-ph↗

arXiv2018

Convex searches for discrete-time Zames-Falb multipliers

In this paper we develop and analyse convex searches for Zames--Falb multipliers. We present two different approaches: Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) multipliers. The set of FIR multipliers is complete in that any IIR multipliers can be phase-substituted by an arbitrarily large order FIR multiplier. We show that searches in discrete-time for FIR multipliers are effective even for large orders. As expected, the numerical results provide the best $\ell_{2}$-stability results in the literature for slope-restricted nonlinearities. Finally, we demonstrate that the discrete-time search can provide an effective method to find suitable continuous-time multipliers.

math.OC↗

arXiv2018

Unified Framework of KKT Conditions Based Matrix Optimizations for MIMO Communications

For multi-input multi-output (MIMO) communication systems, many transceiver design problems involve the optimization of the covariance matrices of the transmitted signals. The derivation of the optimal solutions based on Karush-Kuhn-Tucker (KKT) conditions is a most popular method, and many results have been reported for different scenarios of MIMO systems. In this overview paper, we propose a unified framework in formulating the KKT conditions for general MIMO systems. Based on this framework, the optimal water-filling structures of the transmission covariance matrices are derived rigorously, which are applicable to a wide range of MIMO systems. Our results show that for seemingly different MIMO systems with various power constraints and objective functions, the derivations and water-filling structures for the optimal covariance matrix solutions are fundamentally the same. Thus, our unified framework and solution reveal the underlying relationships among the different water-filling structures of the covariance matrices. Furthermore, our results provide new solutions to the covariance matrix optimization of many complicated MIMO systems with multiple users and imperfect channel state information which were unknown before.

cs.IT↗

arXiv2018

Novel development of dissipative-soliton-resonance pulses with pump power in an all-normal-dispersion fiber laser

Evolution of dissipative-soliton-resonance (DSR) pulses in an all-normal-dispersion fiber laser with pump power under various operation conditions are experimentally studied. The fiber laser is mode-locked by using a nonlinear fiber loop mirror. Apart from the typical pulse broadening due to the peak power clamping effect, pulse breaking was observed. In addition, pulse narrowing with pump power increasing was observed. All the incompatible evolution of DSR pulses could be attributed to the multiple parameters change due to pump power increasing under specific operation conditions.

physics.optics↗

arXiv2018

Orientational migration of halide ion in lead halide perovskites

With the rapid evolution of lead halide perovskite solar cells, there are considerable evidences that the mobile ions strongly impact their properties. However, except the activation energy, the other properties of ionic migration are mostly explored with first principle calculations. Here we investigate the migration of halide ion (vacancy) in lead bromide perovskite single crystalline microplates. In contrast to conventional studies with external bias voltages, we introduce built-in potentials into the microplates via low-energy electron beams and study the corresponding ionic migrations. By aligning the potentials along particular crystal planes, the reduction of Pb2+ to Pb0 has been observed at the un-irradiated center of each cell, demonstrating that the migration of halide ion has preferred directions for the first time. Our observations are consistent with the recent theoretical models and shall open up a new route to improve the performances of perovskite solar cells.

cond-mat.mtrl-sci↗

arXiv2018

MWPC prototyping and performance test for the STAR inner TPC upgrade

A new prototype of STAR inner Time Projection Chamber (iTPC) MWPC sector has been fabricated and tested in an X-ray test system. The wire chamber built at Shandong University has a wire tension precision better than 6$\%$ and wire pitch precision better than 10 $μ$m. The gas gain uniformity and energy resolution are measured to be better than 1$\%$ (RMS) and 20$\%$ (FWHM), respectively, using an $^{55}$Fe X-ray source. The iTPC upgrade project is to replace all 24 STAR TPC inner sectors as a crucial detector upgrade for the RHIC beam energy scan phase II program. The test results show that the constructed iTPC prototype meets all project requirements.

physics.ins-det↗

arXiv2018

Deep Discriminant Analysis for i-vector Based Robust Speaker Recognition

Linear Discriminant Analysis (LDA) has been used as a standard post-processing procedure in many state-of-the-art speaker recognition tasks. Through maximizing the inter-speaker difference and minimizing the intra-speaker variation, LDA projects i-vectors to a lower-dimensional and more discriminative sub-space. In this paper, we propose a neural network based compensation scheme(termed as deep discriminant analysis, DDA) for i-vector based speaker recognition, which shares the spirit with LDA. Optimized against softmax loss and center loss at the same time, the proposed method learns a more compact and discriminative embedding space. Compared with the Gaussian distribution assumption of data and the learnt linear projection in LDA, the proposed method doesn't pose any assumptions on data and can learn a non-linear projection function. Experiments are carried out on a short-duration text-independent dataset based on the SRE Corpus, noticeable performance improvement can be observed against the normal LDA or PLDA methods.

cs.SD↗

arXiv2018

Power Grid Decomposition Based on Vertex Cut Sets and Its Applications to Topology Control and Power Trading

It is well known that the reserves/redundancies built into the transmission grid in order to address a variety of contingencies over a long planning horizon may, in the short run, cause economic dispatch inefficiency. Accordingly, power grid optimization by means of short term line switching has been proposed and is typically formulated as a mixed integer programming problem by treating the state of the transmission lines as a binary decision variable, i.e. in-service or out-of-service, in the optimal power flow problem. To handle the combinatorial explosion, a number of heuristic approaches to grid topology reconfiguration have been proposed in the literature. This paper extends our recent results on the iterative heuristics and proposes a fast grid decomposition algorithm based on vertex cut sets with the purpose of further reducing the computational cost. The paper concludes with a discussion of the possible relationship between vertex cut sets in transmission networks and power trading.

eess.SY↗

arXiv2018

Paradigm and Paradox in Topology Control of Power Grids

Corrective Transmission Switching can be used by the grid operator to relieve line overloading and voltage violations, improve system reliability, and reduce system losses. Power grid optimization by means of line switching is typically formulated as a mixed integer programming problem (MIP). Such problems are known to be computationally intractable, and accordingly, a number of heuristic approaches to grid topology reconfiguration have been proposed in the power systems literature. By means of some low order examples (3-bus systems), it is shown that within a reasonably large class of greedy heuristics, none can be found that perform better than the others across all grid topologies. Despite this cautionary tale, statistical evidence based on a large number of simulations using using IEEE 118- bus systems indicates that among three heuristics, a globally greedy heuristic is the most computationally intensive, but has the best chance of reducing generation costs while enforcing N-1 connectivity. It is argued that, among all iterative methods, the locally optimal switches at each stage have a better chance in not only approximating a global optimal solution but also greatly limiting the number of lines that are switched.

eess.SY↗

arXiv2018

Disentangling Aspect and Opinion Words in Target-based Sentiment Analysis using Lifelong Learning

Given a target name, which can be a product aspect or entity, identifying its aspect words and opinion words in a given corpus is a fine-grained task in target-based sentiment analysis (TSA). This task is challenging, especially when we have no labeled data and we want to perform it for any given domain. To address it, we propose a general two-stage approach. Stage one extracts/groups the target-related words (call t-words) for a given target. This is relatively easy as we can apply an existing semantics-based learning technique. Stage two separates the aspect and opinion words from the grouped t-words, which is challenging because we often do not have enough word-level aspect and opinion labels. In this work, we formulate this problem in a PU learning setting and incorporate the idea of lifelong learning to solve it. Experimental results show the effectiveness of our approach.

cs.CL↗

arXiv2018

Deep Learning for Sentiment Analysis : A Survey

Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction results. Along with the success of deep learning in many other application domains, deep learning is also popularly used in sentiment analysis in recent years. This paper first gives an overview of deep learning and then provides a comprehensive survey of its current applications in sentiment analysis.

cs.CL↗

arXiv2018

Contextual and Position-Aware Factorization Machines for Sentiment Classification

While existing machine learning models have achieved great success for sentiment classification, they typically do not explicitly capture sentiment-oriented word interaction, which can lead to poor results for fine-grained analysis at the snippet level (a phrase or sentence). Factorization Machine provides a possible approach to learning element-wise interaction for recommender systems, but they are not directly applicable to our task due to the inability to model contexts and word sequences. In this work, we develop two Position-aware Factorization Machines which consider word interaction, context and position information. Such information is jointly encoded in a set of sentiment-oriented word interaction vectors. Compared to traditional word embeddings, SWI vectors explicitly capture sentiment-oriented word interaction and simplify the parameter learning. Experimental results show that while they have comparable performance with state-of-the-art methods for document-level classification, they benefit the snippet/sentence-level sentiment analysis.

cs.CL↗

arXiv2018

Superconductivity proximate to antiferromagnetism in a copper-oxide monolayer grown on Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$

A nodeless superconducting (SC) gap was reported in a recent scanning tunneling spectroscopy experiment of a copper-oxide monolayer grown on the Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$ (Bi2212) substrate [Y. Zhong {\it et al.}, Sci. Bull. {\bf 61}, 1239 (2016)], which is in stark contrast to the nodal d-wave pairing gap in the bulk cuprates. Motivated by this experiment, we first show with first-principles calculations that the tetragonal CuO (T-CuO) monolayer on the Bi2212 substrate is more stable than the commonly postulated CuO$_{2}$ structure. The T-CuO monolayer is composed of two CuO$_2$ layers sharing the same O atoms. The band structure is obtained by first-principles calculations, and its strong electron correlation is treated with the renormalized mean-field theory. We argue that one CuO$_2$ sublattice is hole doped while the other sublattice remains half filled and may have antiferromagnetic (AF) order. The doped Cu sublattice can show d-wave SC; however, its proximity to the AF Cu sublattice induces a spin-dependent hopping, which splits the Fermi surface and may lead to a full SC gap. Therefore, the nodeless SC gap observed in the experiment could be accounted for by the d-wave SC proximity to an AF order, thus it is extrinsic rather than intrinsic to the CuO$_2$ layers.

cond-mat.supr-con↗

arXiv2017

A Novel Decomposition for Control of DC Circuits and Grid Models with Heterogeneous Energy Sources

The way in which electric power depends on the topology of circuits with mixed voltage and current sources is examined. The power flowing in any steady-state DC circuit is shown to depend on a minimal set of key variables called fundamental node voltages and fundamental edge currents. Every steady-state DC circuit can be decomposed into a voltage controlled subcircuit and a current controlled subcircuit. In terms of such a decomposition, the I^2R losses of a mixed source circuit are always the sum of losses on the voltage controlled subcircuit and the current controlled subcircuit. The paper concludes by showing that the total power flowing in a mixed source circuit can be found as critical points of the power expressed in terms of the key voltage and current variables mentioned above. The possible relationship to topology control of electric grid operations is discussed.

eess.SY↗