检索结果-内蒙古大学图书馆

Telecommunications, Electronics and Informatics (ICTEI), International Conference on

作者： Wenjia Ma Sichuan University Jinjiang College MeiShan Sichuan China

ISBN: (数字)9798350342963

ISBN: (纸本)9798350342970

In English learning, good pronunciation and intonation are not only the basis for cultivating good listening and speaking abilities, but also have a direct impact on learners’ reading and writing abilities. Traditional English pronunciation teaching methods almost all emphasize theoretical knowledge, while students have fewer practical opportunities to practice, which leads to students’ lack of interest in English pronunciation learning. This paper presents a speech signal recognition method based on ACA, which can help English pronunciation teaching and provide support for intelligent English teaching. The results show that the speech recognition accuracy of this algorithm can reach 98.76%, which is 2.56% higher than that of back propagation neural network (BPNN) and 4.22% higher than that of genetic algorithm (GA). As an auxiliary teaching method, virtual teaching platform is applied to English teaching, which reflects the change of modern teaching philosophy and teaching mode, effectively creates a real English language environment, provides a platform for mutual communication and learning, effectively makes up for the deficiency of traditional English classroom teaching, stimulates learning motivation, stimulates thinking, improves English ability and classroom teaching efficiency.

关键词： Training Accuracy Education Software algorithms Neural networks signal processing algorithms Speech recognition

来源：评论

学校读者我要写书评

暂无评论

Shockwave signal Downsampling Rate Acquisition Based on Sparse Fourier Transform

引用

IEEE ACCESS 2022年 10卷 44076-44087页

作者： Xu, Bo Zhang, Zhi Xie, Zhenglin Han, Tailin Liu, Hong Liu, Xuan Ju, Mingchi Changchun Univ Sci & Technol Sch Elect & Informat Engn Changchun 130022 Peoples R China Changchun Univ Sci & Technol Sch Optoelect Engn Changchun 130022 Peoples R China Changchun Univ Sci & Technol Changchun 130022 Peoples R China

The wireless distributed transient shockwave testing system based on Nyquist sampling needs to keep high sampling rate and front-end data processing rate, which leads to the shortening of the life cycle of wireless nodes. In order to solve the above problems, the shockwave signal acquisition technology based on Sparse Fourier Transform is proposed in this paper. The specific method is to use the sparse characteristics of the frequency domain of the transient shockwave signal to collect the frequency domain by downsampling, reduce the amount of acquired data. The simulation results of the above experiments are compared with the simulation results of Compressed Sensing low sampling observation. The experimental results show that the time-domain acquisition accuracy error of the proposed framework is less than 10(-4) when the Nyquist sampling rate is less than 8 times, and the mean relative error in time domain is only 0.01%. And compared with the acquisition framework based on Compressed Sensing theory, the reconstructed mean absolute error of the proposed method is reduced by 88.66% in the case of the same downsampling multiple. Moreover, this method does not need to store the high-dimensional random observation matrix similar to Compressed Sensing, which overcomes the defect of hardware implementation difficulty.

关键词： Frequency-domain analysis signal processing algorithms Approximation algorithms Wireless sensor networks Wireless communication Transient analysis Sparse matrices Wireless nodes transient shockwave signal sparse fourier transform compressed sensing downsampling

来源：评论

学校读者我要写书评

暂无评论

Balancing Rates and Variance via Adaptive Batch-Size for Stochastic Optimization Problems

引用

IEEE TRANSACTIONS ON signal processing 2022年 70卷 3693-3708页

作者： Gao, Zhan Koppel, Alec Ribeiro, Alejandro Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA Amazon Supply Chain Optimizat Technol Bellevue WA 98004 USA

Stochastic gradient descent is a canonical tool for addressing stochastic optimization problems, which forms the bedrock of modern machine learning. In this work, we seek to balance the fact that attenuating step-size is required for exact convergence with the fact that constant step-size learns faster to a limiting error. To do so, rather than fixing the mini-batch and the step-size at the outset, we propose a strategy to allow parameters evolving adaptively. Specifically, the batch-size is set to be a piecewise-constant increasing sequence where the increase occurs when a suitable error criterion is satisfied. Moreover, the step-size is selected as that which yields the fastest convergence. The overall algorithm, two scale adaptive (TSA) scheme, is developed for both convex and non-convex problems. It inherits the exact convergence and more importantly, the optimal error decreasing rate and an overall computation reduction are achieved. Furthermore, we extended the TSA method to the generalized adaptive batching framework, which is a generic methodology modular to any stochastic algorithms pursuing a trade-off between convergence rates and stochastic variance. We evaluate the TSA method on the image classification problem on MNIST and CIFAR-10 datasets compared with standard SGD methods and existing adaptive batch-size methods, to corroborate theoretical findings.

关键词： Convergence Stochastic processes Optimization Training Statistics Sociology signal processing algorithms Stochastic optimization stochastic gradient descent adaptive batch-size optimal step-size

来源：评论

学校读者我要写书评

暂无评论

Data-Driven Bandpass Filter Design for Estimating Symbol Rate of Sporadic signal at Low SNR

引用

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第4期21卷 2680-2694页

作者： Pei, Can Bi, Suzhi Quan, Zhi Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China

Symbol rate is one of the most important parameters in signal demodulation process. In real-time signal processing, traditional symbol rate estimation algorithms for the Multiple Phase Shift Keying (M-PSK) and the Multiple Quadrature Amplitude Modulation (M-QAM) are based on the Fourier transform of signal's complex envelope. At the low signal-to-noise ratio (SNR), the accuracy of symbol rate estimation can be improved by increasing the number of symbols as much as possible. However, this improvement is infeasible in many applications such as the energy-limited Internet of Things devices and sporadic noncooperative transmissions. In this paper, we propose a data-driven bandpass filter (BPF) design scheme for accurate estimation of symbol rate under low SNR with only a small number of symbols available. The proposed scheme considerably improves the estimation performance by optimizing the BPF design using the equivalent dynamic linearization model with time-varying pseudo-partial derivatives. Specifically, the proposed scheme iteratively optimizes the upper and lower cut-off frequencies of the BPF based on the measured complex envelope spectrum until achieving the optimal BPF. Therefore, the peaks of the complex envelope spectrum are extracted as the estimate of the symbol rate by applying the optimal BPF. Experimental results indicate the promise of the proposed scheme as an efficient symbol rate estimator for sporadic signal at low SNR and with a small number of symbols.

关键词： Estimation Band-pass filters signal to noise ratio Cutoff frequency Wavelet transforms Bandwidth signal processing algorithms Symbol rate estimation data-driven signal processing complex envelope spectrum digital filter design wireless communication

来源：评论

学校读者我要写书评

暂无评论

Short and Wide Network Paths

引用

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2022年第2期9卷 524-537页

作者： Marla, Lavanya Varshney, Lav R. Shah, Devavrat Prakash, Nirmal A. Gale, Michael E. Univ Illinois Dept Ind & Enterprise Syst Engn Urbana IL 61801 USA Univ Illinois Coordinated Sci Lab Urbana IL 61801 USA Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA MIT Inst Data Syst & Soc 77 Massachusetts Ave Cambridge MA 02139 USA MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA Univ Illinois Dept Comp Sci Urbana IL 61801 USA

Network flow is a powerful mathematicalframework to systematically explore the relationship between structure and function in biological, social, and technological networks. We introduce a new pipelining model of flow through networks where commodities must be transported over single paths rather than split over several paths and recombined. We show this notion of pipelined network flow is optimized using network paths that are both short and wide, and develop efficient algorithms to compute such paths for given pairs of nodes and for all-pairs. Short and wide paths are characterized for many real-world networks. Using this framework, we further develop novel information-theoretic lower bounds on computation speed in nervous systems due to limitations from anatomical connectivity and physical noise. This provides predictions on the structural organization and behavior of the nematode Caenorhabditis elegans.

关键词： signal processing algorithms Pipeline processing Neurons Biological system modeling Switching circuits Standards Optimization Network flow shortest paths widest paths

来源：评论

学校读者我要写书评

暂无评论

Online Particle Smoothing With Application to Map-Matching

引用

IEEE TRANSACTIONS ON signal processing 2022年 70卷 497-508页

作者： Duffield, Samuel Singh, Sumeetpal Univ Cambridge Dept Engn Cambridge CB2 1TN England

We introduce a novel method for online smoothing in state-space models that utilises a fixed-lag approximation to overcome the well known issue of path degeneracy. Unlike classical fixed-lag techniques that only approximate certain marginals, we introduce an online resampling algorithm, called particle stitching, that converts these marginal samples into a full posterior approximation. We demonstrate the utility of our method in the context of map-matching, the task of inferring a vehicle's trajectory given a road network and noisy GPS observations. We develop a new state-space model for the difficult task of map-matching on dense, urban road networks.

关键词： Smoothing methods Approximation algorithms State-space methods Additives Trajectory signal processing algorithms Task analysis State-space models sequential Monte Carlo particle smoothing backward simulation map-matching GPS

来源：评论

学校读者我要写书评

暂无评论

Considerations for Antenna Design and signal processing in Radar-Based Hallway Gait Monitoring

Considerations for Antenna Design and Signal Processing in R...

引用

URSI International Symposium on Electromagnetic Theory (EMTS)

作者： Hajar Abedi Ahmad Ansariyan Plinio P Morita Jennifer Boger Alexander Wong George Shaker University of Waterloo Waterloo Canada

ISBN: (数字)9798350399288

ISBN: (纸本)9798350399295

In this study, we examined the use of commercially available radars for monitoring gait in hallways. To address the challenges of this task, we evaluated two approaches: 1) modifying the radar antenna using a hyperbolic lens and 2) a novel signal processing method. For the first approach, we designed an add-on lens to improve the beam focus of a commercial radar system. For the second approach, we proposed an algorithm that can be used with any type of MIMO FMCW radar to detect human gait in a hallway without requiring modifications to the radar antenna. Our results showed that both methods were able to extract gait values with similar accuracy, but the signal processing method was computationally intensive, while the antenna modification method required less computational power.

关键词： Radar detection signal processing algorithms Radar Radar antennas Radar signal processing Spatiotemporal phenomena Dielectrics Electromagnetics Monitoring Lenses

来源：评论

学校读者我要写书评

暂无评论

Explainability in Graph Data Science Interpretability, replicability, and reproducibility of community detection

引用

IEEE signal processing MAGAZINE 2022年第4期39卷 25-39页

作者： Aviyente, Selin Karaaslanli, Abdullah Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA Michigan State Univ Elect & Comp Engn E Lansing MI 48824 USA

In many modern data science problems, data are represented by a graph (network), e.g., social, biological, and communication networks. Over the past decade, numerous signal processing and machine learning (ML) algorithms have been introduced for analyzing graph structured data. With the growth of interest in graphs and graph-based learning tasks in a variety of applications, there is a need to explore explainability in graph data science. In this article, we aim to approach the issue of explainable graph data science, focusing on one of the most fundamental learning tasks, community detection, as it is usually the first step in extracting information from graphs. A community is a dense subnetwork within a larger network that corresponds to a specific function. Despite the success of different community detection methods on synthetic networks with strong modular structure, much remains unknown about the quality and significance of the outputs of these algorithms when applied to real-world networks with unknown modular structure. Inspired by recent advances in explainable artificial intelligence (AI) and ML, in this article, we present methods and metrics from network science to quantify three different aspects of explainability, i.e., interpretability, replicability, and reproducibility, in the context of community detection.

关键词： Machine learning algorithms signal processing algorithms Machine learning Data science signal processing Reproducibility of results

来源：评论

学校读者我要写书评

暂无评论

Centralized and Distributed Robust State Estimation Over Sensor Networks Using Elliptical Distribution

引用

IEEE INTERNET OF THINGS JOURNAL 2022年第21期9卷 21825-21837页

作者： Wang, Guoqing Yang, Chunyu Ma, Lei Dai, Wei China Univ Min & Technol Engn Res Ctr Intelligent Control Underground Spac Minist Educ Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China

We consider the robust state estimation over sensor networks with non-Gaussian noise, which is often encountered in many applications. Motivated by the fact that the elliptical distribution is the natural extension of the Gaussian distribution and includes a variety of non-Gaussian distributions with heavy-tailed characteristics, we here adopt the elliptical distribution to model the heavy-tailed process and measurement noise. The general state evolution model is used to replace the process equation and the elliptical distribution is denoted as a Gaussian mixture form. Based on that, the posterior estimation of the system state together with the parameters of the process and measurement noises can be inferred by the variational Bayes method. Moreover, the corresponding distributed estimation algorithm is then provided, which enables distributed implementation. The target tracking over sensor networks is used to show the estimation accuracy of the proposed algorithms.

关键词： State estimation signal processing algorithms Noise measurement Mathematical models Sea measurements Internet of Things Gaussian distribution Elliptical distribution robust state estimation sensor networks variational Bayes (VB)

来源：评论

学校读者我要写书评

暂无评论

SNR Improvement for Maneuvering Ship Using Weak Echo Under the Condition of Beidou GEO Satellites

引用

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 1256-1271页

作者： Li, Yan Yan, Songhua Gong, Jianya Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430072 Peoples R China

Using global navigation satellite systems as transmitters of opportunity for target detection is a hot issue. The problems of this technology are the restricted power budget provided by navigation satellites and the defocusing caused by the movement of target. To refocus the echo and improve the signal-to-noise ratio under the condition of Beidou Geostationary satellites, this article uses jointly the short-time coherent integration and the long-time integration based on Fractional Fourier transform to correct the range migration and the Doppler frequency migration of signals for refocusing the signal energy. The effectiveness of the proposed algorithm for refocusing the echo from different types of maneuvering ships (a cargo ship and a ferry) is confirmed via an experimental campaign.

关键词： signal to noise ratio Satellites Global navigation satellite system Doppler effect signal processing algorithms Passive radar Spaceborne radar Beidou geostationary (GEO) satellites global navigation satellite systems (GNSS) maneuvering ships signal-to-noise ratio (SNR) improvement

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：