IEEE Transactions on Signal Processing最新文献_第10页

Detection and Multiparameter Estimation for NLoS Targets: An RIS-Assisted Framework NLoS 目标的探测和多参数估计：RIS 辅助框架

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-17 DOI: 10.1109/TSP.2025.3546991

Zhouyuan Yu;Xiaoling Hu;Chenxi Liu;Qin Tao;Mugen Peng

{"title":"Detection and Multiparameter Estimation for NLoS Targets: An RIS-Assisted Framework","authors":"Zhouyuan Yu;Xiaoling Hu;Chenxi Liu;Qin Tao;Mugen Peng","doi":"10.1109/TSP.2025.3546991","DOIUrl":"10.1109/TSP.2025.3546991","url":null,"abstract":"Reconfigurable intelligent surface (RIS) has the potential to enhance sensing performance, due to its capability of reshaping the echo signals. Different from the existing literature, which has commonly focused on RIS beamforming optimization, in this paper, we pay special attention to designing effective signal processing approaches to extract sensing information from RIS-reshaped echo signals. To this end, we investigate an RIS-assisted non-line-of-sight (NLoS) target detection and multi-parameter estimation problem in orthogonal frequency division multiplexing (OFDM) systems. To address this problem, we first propose a novel detection and direction estimation framework, including a low-overhead hierarchical codebook that allows the RIS to generate three-dimensional beams with adjustable beam direction and width, a delay spectrum peak-based beam training scheme for detection and direction estimation, and a beam refinement scheme for further enhancing the accuracy of the direction estimation. Then, we propose a target range and velocity estimation scheme by extracting the delay-Doppler information from the RIS-reshaped echo signals. Numerical results demonstrate that the proposed schemes can achieve a <inline-formula><tex-math>$99.7%$</tex-math></inline-formula> target detection rate, a <inline-formula><tex-math>$10^{-3}$</tex-math></inline-formula>-rad level direction estimation accuracy, and a <inline-formula><tex-math>$10^{-6}$</tex-math></inline-formula>-m/<inline-formula><tex-math>$10^{-5}$</tex-math></inline-formula>-m/s level range/velocity estimation accuracy.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1470-1484"},"PeriodicalIF":4.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143640563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Denoising Piecewise Constant Nanopore Signals 分段常数纳米孔信号去噪

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-15 DOI: 10.1109/TSP.2025.3570417

Adrian Vidal;Emanuele Viterbo

{"title":"Denoising Piecewise Constant Nanopore Signals","authors":"Adrian Vidal;Emanuele Viterbo","doi":"10.1109/TSP.2025.3570417","DOIUrl":"10.1109/TSP.2025.3570417","url":null,"abstract":"Nanopore sequencing signals can be described as indirect noisy observations that reflect the instantaneous conductance of the nanopore channel as an analyte DNA molecule translocates through the pore in real time, with <inline-formula><tex-math>$delta$</tex-math></inline-formula> nucleotides (<inline-formula><tex-math>$delta$</tex-math></inline-formula>-mers) blocking the pore at any instant. The sequence of overlapping <inline-formula><tex-math>$delta$</tex-math></inline-formula>-mers along the ssDNA molecule are thus indirectly observed as a sequence of conductance levels (i.e., a <italic>signature) that is used to characterize its DNA sequence. In this paper, we denoise piecewise constant nanopore signals drawn from the same Gaussian-output, left-to-right hidden Markov model (HMM) and recover the unknown signature that is used to parameterize the HMM. We place a Gaussian prior on the signature and use importance sampling to approximate the minimum mean-square error estimate (MMSE) of the signature given the signals. To circumvent the difficulty of sampling from the true posterior, we construct a proposal distribution from which the joint segmentation of the observed signals can be efficiently sampled in <inline-formula><tex-math>$O(Mn^{2}k)$</tex-math></inline-formula> time, where <inline-formula><tex-math>$ M $</tex-math></inline-formula> is the number of signals, <inline-formula><tex-math>$ n $</tex-math></inline-formula> is the average duration of each signal, and <inline-formula><tex-math>$ k $</tex-math></inline-formula> is the length of the signature. Finally, we evaluate the performance of the algorithm using both simulated and experimental nanopore signals generated by Oxford Nanopore Technologies’ (ONT) R10.4.1 nanopore. The proposed method can be effective in constructing accurate <inline-formula><tex-math>$delta$</tex-math></inline-formula>-mer tables used to fully characterize all the <inline-formula><tex-math>$4^{delta}$</tex-math></inline-formula> states of any nanopore sequencer.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1993-2007"},"PeriodicalIF":4.6,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144066008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automorphism Ensemble Decoding on GPU: Achieving High Throughput and Low Latency for Polar and RM Codes GPU上的自同构集成解码：实现极性和RM码的高吞吐量和低延迟

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-15 DOI: 10.1109/TSP.2025.3570740

Yansong Li;Kairui Tian;Rongke Liu

{"title":"Automorphism Ensemble Decoding on GPU: Achieving High Throughput and Low Latency for Polar and RM Codes","authors":"Yansong Li;Kairui Tian;Rongke Liu","doi":"10.1109/TSP.2025.3570740","DOIUrl":"10.1109/TSP.2025.3570740","url":null,"abstract":"Automorphism ensemble decoding (AED) is a highly parallel approach that enables decoding of polar and Reed-Muller (RM) codes with automorphisms, offering a practical solution with near-maximum likelihood (ML) performance and manageable computational complexity. To meet the growing demands for high throughput and low latency in cloud and virtual random access networks, this paper presents a graphics processing unit (GPU)-based AED architecture for polar and RM codes, utilizing low-complexity successive cancellation (SC) and small list SC (SCL) decoders as the constituent of AED. The proposed architecture exploits the inherent parallelism of AED to optimize decoding tasks on the GPU, significantly enhancing throughput by efficiently harnessing the massive parallel processing capabilities of the GPU. Additionally, improved thread mapping and data management techniques substantially reduce latency for automorphism ensemble SC (Aut-SC) decoding, while a low-latency sorting mechanism further accelerates automorphism ensemble SCL (Aut-SCL) decoding. Experimental results on an NVIDIA RTX 4090 demonstrate that the proposed Aut-SC decoder, with an ensemble size of 8, achieves a throughput exceeding 17 Gbps under highly parallelized batch processing. Compared to the state-of-the-art software-based SCL decoders, the proposed GPU-based Aut-SC and Aut-SCL architectures outperform existing solutions by factors of up to 28$boldsymbol{times}$ and 10$boldsymbol{times}$, respectively, in normalized throughput while maintaining the same or even superior error correction performance.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2227-2242"},"PeriodicalIF":4.6,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144066153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Single-Timescale Multi-Sequence Stochastic Approximation Without Fixed Point Smoothness: Theories and Applications 无不动点平滑的单时间尺度多序列随机逼近：理论与应用

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-14 DOI: 10.1109/TSP.2025.3569665

Yue Huang;Zhaoxian Wu;Shiqian Ma;Qing Ling

{"title":"Single-Timescale Multi-Sequence Stochastic Approximation Without Fixed Point Smoothness: Theories and Applications","authors":"Yue Huang;Zhaoxian Wu;Shiqian Ma;Qing Ling","doi":"10.1109/TSP.2025.3569665","DOIUrl":"10.1109/TSP.2025.3569665","url":null,"abstract":"Stochastic approximation (SA) that involves multiple coupled sequences, also known as multiple-sequence SA (MSSA), finds diverse applications in the fields of signal processing and machine learning. However, existing theoretical understandings of MSSA are limited: the multi-timescale analysis implies a slow convergence rate, whereas the single-timescale analysis relies on a stringent fixed point smoothness assumption. In this paper, we establish tighter single-timescale analysis for MSSA, without assuming smoothness of the fixed points. Our theoretical findings reveal that, when all involved operators are strongly monotone, MSSA converges at a rate of <inline-formula><tex-math>$tilde{mathcal{O}}(K^{-1})$</tex-math></inline-formula>, where <inline-formula><tex-math>$K$</tex-math></inline-formula> denotes the total number of iterations. When all involved operators are strongly monotone except for the main one, MSSA converges at a rate of <inline-formula><tex-math>$O(K^{-frac{1}{2}})$</tex-math></inline-formula>. These rates align with the ones established for single-sequence SA. Applying our novel theoretical findings to bilevel optimization and communication-efficient distributed learning offers relaxed assumptions and/or simpler algorithms with perfor- mance guarantees, as validated by numerical experiments.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1939-1953"},"PeriodicalIF":4.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143979756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Generalized Approach for Recovering Time Encoded Signals With Finite Rate of Innovation 有限创新率下恢复时间编码信号的一种广义方法

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-14 DOI: 10.1109/TSP.2025.3567443

Dorian Florescu

{"title":"A Generalized Approach for Recovering Time Encoded Signals With Finite Rate of Innovation","authors":"Dorian Florescu","doi":"10.1109/TSP.2025.3567443","DOIUrl":"10.1109/TSP.2025.3567443","url":null,"abstract":"In this paper, we consider the problem of reconstructing a function <inline-formula><tex-math>$g(t)$</tex-math></inline-formula> from its direct time encoding machine (TEM) measurements in a general scenario in which the signal is represented as an infinite sum of weighted generic functions <inline-formula><tex-math>$varphi(t)$</tex-math></inline-formula> shifted in real time points. These functions belong to the class of signals with finite rate of innovation (FRI), which is more general than shift-invariant or bandlimited spaces, for which recovery guarantees were already introduced. For an FRI signal <inline-formula><tex-math>$g(t)$</tex-math></inline-formula>, recovery guarantees from their direct TEM samples were introduced for particular functions <inline-formula><tex-math>$varphi(t)$</tex-math></inline-formula> or functions <inline-formula><tex-math>$varphi(t)$</tex-math></inline-formula> with alias cancellation properties leading to <inline-formula><tex-math>$g(t)$</tex-math></inline-formula> being periodic and bandlimited. On the theoretical front, this work significantly increases the class of functions for which reconstruction is guaranteed, and provides a condition for perfect input recovery depending on the first two local derivatives of <inline-formula><tex-math>$varphi(t)$</tex-math></inline-formula>. We extend this result with reconstruction guarantees in the case of noise corrupted FRI signals. On the practical front, we validate the proposed method via numerical simulations with filters previously used in the literature, as well as filters that are not compatible with the existing results. In cases where the filter has an unknown mathematical function and is only measured, the proposed method streamlines the recovery process by bypassing the filter modelling stage. Additionally, we validate the proposed method using a TEM hardware implementation.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1862-1876"},"PeriodicalIF":4.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143979640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal TOA-Based Sensor-Anchor-Source Geometries and Estimation Bounds for Simultaneous Sensor and Source Localization 基于toa的最优传感器锚源几何和同时定位的估计界

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-14 DOI: 10.1109/TSP.2025.3545928

Sheng Xu;Bing Zhu;Xinyu Wu;Kutluyıl Doğançay

{"title":"Optimal TOA-Based Sensor-Anchor-Source Geometries and Estimation Bounds for Simultaneous Sensor and Source Localization","authors":"Sheng Xu;Bing Zhu;Xinyu Wu;Kutluyıl Doğançay","doi":"10.1109/TSP.2025.3545928","DOIUrl":"10.1109/TSP.2025.3545928","url":null,"abstract":"This paper focuses on optimal time-of-arrival (TOA) sensor placement for simultaneous sensor and source localization (SSSL) with the help of selected anchors at known positions in the environment. Firstly, the problem of sensor placement for SSSL is analyzed and formulated as an optimization task based on the approximate Cramér-Rao lower bound (CRLB), which is an approximation of the intractable true CRLB. Secondly, by minimizing the trace of the approximate CRLB, the optimal accuracy bounds for the estimated sensor and source positions are derived, which can serve as a useful evaluation metric for other studies. Thirdly, a systematic solution including both the analytical and algebraic methods is proposed to obtain the optimal sensor-anchor-source geometries for achieving the approximate bounds simultaneously. Significantly, the analytical sensor placement approach can quickly offer an optimal placement for some special cases, and the algebraic algorithm can provide a (sub-)optimal solution numerically for the general case. Furthermore, theoretical guidance for placing anchors in the localization area is provided. Finally, the theoretical findings and proposed algorithms are verified by computer simulations and experimental studies, demonstrating that the optimized sensor positions yield accurate performance. The results in this paper can be utilized as an evaluation tool and a performance improvement guidance for practical SSSL problems.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1727-1743"},"PeriodicalIF":4.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143635660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Parameter Estimation on Homogeneous Spaces 齐次空间的参数估计

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-13 DOI: 10.1109/TSP.2025.3567361

Shiraz Khan;Gregory S. Chirikjian

{"title":"Parameter Estimation on Homogeneous Spaces","authors":"Shiraz Khan;Gregory S. Chirikjian","doi":"10.1109/TSP.2025.3567361","DOIUrl":"10.1109/TSP.2025.3567361","url":null,"abstract":"The <italic>Fisher Information Metric (FIM) and the associated <italic>Cramér-Rao Bound (CRB) are fundamental tools in statistical signal processing, informing the efficient design of experiments and algorithms for estimating the underlying parameters. In this article, we investigate these concepts for the case where the parameters lie on a <italic>homogeneous space. Unlike the existing Fisher-Rao theory for general Riemannian manifolds, our focus is to leverage the group-theoretic structure of homogeneous spaces, which is often much easier to work with than their Riemannian structure. The FIM is characterized by identifying the homogeneous space with a coset space, the group-theoretic CRB and its corollaries are presented, and its relationship to the Riemannian CRB is clarified. The application of our theory is illustrated using two examples from engineering: (i) estimation of the pose of a robot and (ii) sensor network localization. In particular, these examples demonstrate that homogeneous spaces provide a natural framework for studying statistical models that are <italic>invariant with respect to a group of symmetries.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2110-2122"},"PeriodicalIF":4.6,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11002750","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143946129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Boosting RPCA by Prior Subspace 基于先验子空间的RPCA提升

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-13 DOI: 10.1109/TSP.2025.3569861

Lin Chen;Li Ge;Xue Jiang;Hongbin Li

{"title":"Boosting RPCA by Prior Subspace","authors":"Lin Chen;Li Ge;Xue Jiang;Hongbin Li","doi":"10.1109/TSP.2025.3569861","DOIUrl":"10.1109/TSP.2025.3569861","url":null,"abstract":"This paper introduces a novel method, boosting principal component analysis (BPCA), to address the challenge of extracting principal components in the presence of sparse outliers. Building on the traditional robust principal component analysis (RPCA) model, BPCA incorporates prior subspace information through a flexible weighting scheme, enhancing its robustness against the bias in prior subspaces. We develop a novel metric, based on QR decomposition, to assess the accuracy of a prior subspace, which facilitates the analysis of BPCA’s exact recovery feasibility. The exact recovery is achievable if the bias of the prior subspace meets a specific tolerance condition. We establish its recovery guarantee by introducing new incoherence conditions, which offer improved interpretability over existing conditions due to the boosting of prior subspaces. BPCA enjoys a more relaxed recovery bound than RPCA and traditional prior subspace-based methods, provided that the prior subspace is sufficiently accurate, though not necessarily perfect. The necessary level of accuracy for this relaxation is quantified, with an analysis using the convex geometry of the nuclear norm. Furthermore, the proposed BPCA model is scalable and successfully extended to three-dimensional scenes. Experimental results demonstrate the superior performance of BPCA over RPCA and traditional prior subspace-based methods in low-rank recovery. The code of the proposed methods is released at <uri>https://github.com/linchenee/BPCA-BTPCA</uri>.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2170-2186"},"PeriodicalIF":4.6,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143946115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Asymptotic Matrix-Variate Bingham Distributions and Privacy-Preserving Principal Component Analysis 渐近矩阵变量Bingham分布与隐私保护主成分分析

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-13 DOI: 10.1109/TSP.2025.3569636

Lu Wei;Tianxi Ji

引用次数: 0

Greedy Selection for Heterogeneous Sensors 异构传感器的贪婪选择

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-12 DOI: 10.1109/TSP.2025.3549301

Kaushani Majumder;Sibi Raj B. Pillai;Satish Mulleti

{"title":"Greedy Selection for Heterogeneous Sensors","authors":"Kaushani Majumder;Sibi Raj B. Pillai;Satish Mulleti","doi":"10.1109/TSP.2025.3549301","DOIUrl":"10.1109/TSP.2025.3549301","url":null,"abstract":"Simultaneous operation of all sensors in a large-scale sensor network is power-consuming and computationally expensive. Hence, it is desirable to select fewer sensors. A greedy algorithm is widely used for sensor selection in homogeneous networks with a theoretical worst-case performance of <inline-formula><tex-math>$boldsymbol{(mathbf{1-1}/mathbf{e})mathbf{approx 63}}$</tex-math></inline-formula>% of the optimal performance when optimizing submodular metrics. For heterogeneous sensor networks (HSNs) comprising multiple sets of sensors, most of the existing sensor selection methods optimize the performance constrained by a budget on the total value of the selected sensors. However, in many applications, the number of sensors to select from each set is known apriori and solutions are not well-explored. For this problem, we propose a joint greedy heterogeneous sensor selection algorithm. Theoretically, we show that the worst-case performance of the proposed algorithm is bounded to <inline-formula><tex-math>$50$</tex-math></inline-formula>% of the optimum for submodular cost metrics. In the special case of HSNs with two sensor networks, the performance guarantee can be improved to <inline-formula><tex-math>$63$</tex-math></inline-formula>% when the number of sensors to select from one set is much smaller than the other. To validate our results experimentally, we propose a submodular metric based on the frame potential measure that considers both the correlation among the sensor measurements and their heterogeneity. We prove theoretical bounds for the mean squared error of the solution when this performance metric is used. We validate our results through simulation experiments considering both linear and non-linear measurement models corrupted by additive noise and quantization errors. Our experiments show that the proposed algorithm results in <inline-formula><tex-math>$4 {boldsymbol{mathbf{-}}} 10$</tex-math></inline-formula> dB lower error than existing methods.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1394-1409"},"PeriodicalIF":4.6,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143608003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0