International Conference on Signal Processing Systems最新文献_第10页

Monitoring of leaf nitrogen content in a citrus orchard by Landsat 8 OLI imagery 利用Landsat 8 OLI影像监测柑橘园叶片氮含量

International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2589452

Ling-Xiao Liu, Yong Li, Tong Wu

{"title":"Monitoring of leaf nitrogen content in a citrus orchard by Landsat 8 OLI imagery","authors":"Ling-Xiao Liu, Yong Li, Tong Wu","doi":"10.1117/12.2589452","DOIUrl":"https://doi.org/10.1117/12.2589452","url":null,"abstract":"Nitrogen is an essential nutrient for citrus growth. Thus, the chemical analysis of leaf tissues is needed to determine nitrogen in the traditional agronomic method, which is time consuming, labor intensive, and costly. Satellite remote sensing (RS) can quickly acquire multispectral images of large-scale orchards and thus can support low-cost and periodic monitoring of nitrogen content in orchards. RS data have been widely used for the monitoring of nitrogen content in various crops and performed quite well in related researches. However, few studies have been conducted to evaluate the leaf nitrogen content (LNC) of citrus on the basis of the data acquired by satellite RS. In this study, Landsat 8 RS image data are used to estimate the distribution of LNC in an orchard, and the effectiveness of different estimation methods for monitoring LNC value is studied. Linear regression, partial least square regression (PLSR), support vector regression (SVR), random forest regression (RF), and deep neural network (DNN) models are constructed and compared. Experimental results demonstrate the feasibility of using satellite RS data in determining LNC in sugar citrus. In terms of evaluating LNC, the PLSR algorithm outperforms other algorithms in testing data, reaching a determination coefficient of 0.864, a root mean squared error of 1.217, and a mean relation error of 3.5%. An accurate spatial distribution of nitrogen content in an orchard can be obtained by our model, which can be used to provide powerful support for the practical management and operation of the orchard.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126482206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Bernoulli track-before-detect filter for passive radar 无源雷达的伯努利探测前跟踪滤波器

International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2589243

Cong Xu, Zishu He, Lizhi Tang

引用次数: 0

Fall detection method using Wi-Fi channel state information 使用Wi-Fi通道状态信息的跌落检测方法

International Conference on Signal Processing Systems Pub Date : 2019-12-31 DOI: 10.1117/12.2559790

Yaxin Ran, Jiang Yu, Jun Chang, Zheng Zhang

引用次数: 0

Polynomial rotation-polynomial Fourier transform of ultrafast maneuvering targets detection 多项式旋转-多项式傅里叶变换的超快机动目标检测

International Conference on Signal Processing Systems Pub Date : 2019-12-31 DOI: 10.1117/12.2557052

Ning Lv, Yibin Rui, Liyan Wang, Huan Wang, Chenguang Bian

引用次数: 0

An opinion-unaware blind quality assessment algorithm for multiply distorted images 一种不受意见影响的多重失真图像质量盲评估算法

International Conference on Signal Processing Systems Pub Date : 2019-12-31 DOI: 10.1117/12.2559541

Tongle Wang, Junchen Deng

引用次数: 0

Lifting wavelet denoising based on pulsar wavelet basis 基于脉冲星小波基的提升小波去噪

International Conference on Signal Processing Systems Pub Date : 2019-12-31 DOI: 10.1117/12.2559602

S. You, Hongli Wang, Lei Feng, Yiyang He, Qiang Xu, Yongqiang Xiao

{"title":"Lifting wavelet denoising based on pulsar wavelet basis","authors":"S. You, Hongli Wang, Lei Feng, Yiyang He, Qiang Xu, Yongqiang Xiao","doi":"10.1117/12.2559602","DOIUrl":"https://doi.org/10.1117/12.2559602","url":null,"abstract":"In the X-ray pulsar navigation process, since the pulsar signal obtained by the epoch folding contains a large amount of noise, the signal must be denoised in order to obtain higher positioning accuracy. In order to further optimize the denoising effect and improve the algorithm in real time, this paper proposes a pulsar wavelet base and implements its lifting scheme. In this paper, wavelet multi-level decomposition is performed on the pulsar outline, then a wavelet base based on the pulsar's own signal is constructed according to the low-frequency coefficients, and its lifting method is realized. Matlab simulation shows that compared with db4 and db5 methods, the proposed method performs better in terms of signal-to-noise ratio, mean square error, peak relative error, peak position error and real-time performance. Although the peak error of the db1 wavelet is relatively small, its signal-to-noise ratio is too large, and the overall performance is obviously not as good as the proposed method. The proposed signal-to-noise ratio is up to 4.2dB higher than the db4 and db5 methods, and the mean square error is only 24.3% of the db4 and db5 methods. The peak position error is only 50% of the db4 and db5 methods.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114628698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Frame-level speech enhancement based on Wasserstein GAN 基于Wasserstein GAN的帧级语音增强

International Conference on Signal Processing Systems Pub Date : 2019-12-31 DOI: 10.1117/12.2559619

Peng Chuan, Tian Lan, M. Li, Sen Li, Qiao Liu

引用次数: 0

NIWPT: NLOS identification based on channel state information NIWPT:基于信道状态信息的NLOS识别

International Conference on Signal Processing Systems Pub Date : 2019-12-31 DOI: 10.1117/12.2559766

Chunyuan Tian, Jiang Yu, Jun Chang, Yonghong Zhang

{"title":"NIWPT: NLOS identification based on channel state information","authors":"Chunyuan Tian, Jiang Yu, Jun Chang, Yonghong Zhang","doi":"10.1117/12.2559766","DOIUrl":"https://doi.org/10.1117/12.2559766","url":null,"abstract":"With the development of wireless technology, Wi-Fi devices are extensively deployed in indoor environments. This fosters the development of Wi-Fi signal-based services and applications, e.g., indoor intrusion detection, human gesture recognition, indoor localization. However, the indoor environments are often complex and variable, Wi-Fi signals from transmitters through multiple paths to reach receivers. There is a large number of Non-Line-Of-Sight (NLOS) paths between the transmitter and the receiver, which causes seriously signal fading, deteriorating the quality of communication links, decreasing the accuracy of recognition application, and increasing the complexity of systems. In this study, an NLOS identification based on the wavelet packet transform (NIWPT) method is proposed. First, NIWPT collects raw channel state information (CSI) signals on the physical layer in current links. Then, NIWPT applies threelayer wavelet packet decomposition on the amplitude of CSI. A set of the wavelet packet coefficient, wavelet packet energy spectrum, information entropy, and logarithmic energy entropy as a feature vector is acquired. After that, the support vector machine is utilized to identify NLOS paths in the current links. Compared with other methods, NIWPT does not need to pre-process the raw CSI signals, it not only maximally reserves influence of the environment on the propagation signal, but also reflects the indoor environment more truly. The experimental results indicate that the recognition accuracy rate of the NIWPT method is 96.23% and 94.75% in the dynamic and static environments, respectively. It proves that the proposed method can effectively identify NLOS paths and has high identification accuracy and universality.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"11384 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128935212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multiple target DOA estimation with single snapshot in sonar array 声纳阵中单快照多目标DOA估计

International Conference on Signal Processing Systems Pub Date : 2019-12-31 DOI: 10.1117/12.2559907

Jiani Wu, C. Bao

引用次数: 3

A robust adaptive amplitude iteration CFAR detector in nonhomogeneous clutter environment 非均匀杂波环境下鲁棒自适应幅度迭代CFAR检测器

International Conference on Signal Processing Systems Pub Date : 2019-12-31 DOI: 10.1117/12.2557649

Renhong Xie, Liyan Wang, Zeyu Sun, Chenguang Bian, Ning Lv, Huan Wang, Peng Li, Yibin Rui

引用次数: 0