英语论文引言句

介绍一个【CS 计算机】，【EE 信号处理】领域的introduction 通用写作模板,模板将introduction分成5段，供大家参考。在每段英文后面，配备了相对应的中文，以方便大家阅读。本文格式，包括引用都是按照IEEE 的格式写的。【第一段】研究意义，也可以叫研究的作用，或者称为研究的应用场景。格式如下:（topic）has been extensively studied in the literature. Some major applications of （topic） include speech processing, e.g. [1]-[3], Video Surveillance, e.g. [4], [5], biomedical image processing, e.g. [6], and sensor fusion, e.g. [7], [8].（某某研究）已经在学术界被广泛的研究，（某某研究）d的应用场景包括语音信号处理，例如[1]-[3], 视频监控，例如 [4], [5], 医学图像处理，例如[6], 和传感器融合，例如 [7], [8].【第二段】以前类似的研究，以及它们的不足之处。格式如下:Different approaches have been utilized for (topic). These approaches consist of approaches A, e.g. [ ], approaches B, e.g. [ ], and approaches C, e.g. [ ]. Each of these approaches has its strengths and limitations. Approaches A provide (strengths ). However, (limitations). Approaches B provide (strengths ). However, (limitations). Approaches C provide (strengths ). However, (limitations). Basically, no approaches is perfect, that is (issues have not been addressed).针对（某某研究）已经提出了很多种方法。这些方法包括方法A[ ]，方法B[ ]和方法C[ ]。每种方法都有其优势和劣势。方法A（优势），但是（劣势）。方法B（优势），但是（劣势）。方法C（优势），但是（劣势）。没有一种方法可以完全解决XXX问题。【第三段】针对以前研究的不足之处（或者没有解决的问题），本文做出的改进。格式如下:In order to (solve XX problem), (a new approach ) has been used. (Details about this new approach).为了解决XX问题，本文提出了一种XXX的方法。（XXX方法的概括描述）。【第四段】罗列本文主要创新点。格式如下:This paper differs from the previous papers by the following aspect: 1) (The first different); 2) (The second different); 3) (The third different).本文提出的方法主要有如下几个创新点。1）一句话概括创新点； 2）一句话概括创新点； 3）一句话概括创新点。【第五段】本文剩余部分介绍。格式如下:The remainder of this paper is organized as follows. The dataset of XXX is described in Section Ⅱ. In Section Ⅲ, the details of the developed XXX approach are stated. The experimental results are then reported in Section Ⅳ. Finally, Section V concludes the paper.本文剩下的部分是这样组织的。第二章描述了XXXX（一般是数据集，或者以前的方法）。第三章介绍了本文提出的XX方法和其技术细节。第四章报告了实验结果并作出了分析。第五章总结了全文。[1]H. Wei and N. Kehtarnavaz, "Determining Number of Speakers from Single Microphone Speech Signals by Multi-Label Convolutional Neural Network,"IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, 2018, pp. 2706-2710.[2]Y. Zhang, Y. Long, X. Shen, H. Wei, M. Yang, H. Ye and H. Mao, "Articulatory movement features for short-duration text-dependent speaker verification," International Journal of Speech Technology, vol. 20, no.1, pp:1-7, 2017.[3] H. Wei, Y. Long and H. Mao, "Improvements on self-adaptive voice activity detector for telephone data," International Journal of Speech Technology, vol.19, no.3, pp:1-8, 2016.[4] H. Wei and N. Kehtarnavaz, "Semi-Supervised Faster RCNN-Based Person Detection and Load Classification for Far Field Video Surveillance," Machine Learning and Knowledge Extraction, vol. 1, no. 3, pp. 756–767, June 2019.[5] H. Wei, M. Laszewski and N. Kehtarnavaz, "Deep Learning-Based Person Detection and Classification for Far Field Video Surveillance,"2018 IEEE 13th Dallas Circuits and Systems Conference (DCAS), Dallas, TX, 2018, pp. 1-4.[6]H. Wei, A. Sehgal and N. Kehtarnavaz," A Deep Learning-Based Smartphone App for Real-Time Detection of Retinal Abnormalities in Fundus Images," SPIE conference of Real-Time Image Processing and Deep Learning, Baltimore,May 2019.[7] H. Wei, R. Jafari and N. Kehtarnavaz, “Fusion of Video and Inertial Sensing for Deep Learning–Based Human Action Recognition,” Sensors, vol. 19, no. 17, p. 3680, Aug. 2019.[8] H. Wei and N. Kehtarnavaz, "Simultaneous utilization of inertial and video sensing for action detection and recognition in continuous action streams," IEEE Sensors Journal, 2020.